import tensorflow as tf import numpy as np from sklearn.utils import shuffle from sklearn.preprocessing import MinMaxScaler from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Activation, Dense from tensorflow.keras.optimizers import Adam from tensorflow.keras.metrics import categorical_crossentropy %matplotlib from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay import itertools import matplotlib.pyplot as plt import os.path from tensorflow.keras.models import load_model from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Conv2D from tensorflow.keras.models import Model from tensorflow.keras.applications.vgg19 import VGG19 from tensorflow.keras.applications.resnet50 import preprocess_input from tensorflow.keras.preprocessing import image from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img from tensorflow.keras.models import Sequential import numpy as np from glob import glob import matplotlib.pyplot as plt from tensorflow.keras.layers import MaxPooling2D

import numpy as np from enum import Enum from random import random, randint, choice from collections import defaultdict from typing import List, Callable, Tuple from copy import deepcopy # Generate Gridworld Function def gen_gridworld(dim:int, prob:float, solvable: bool = None, is_terrain_enabled: bool = True, source: Tuple[int, int] = None, target: Tuple[int, int] = None) -> (List[List[float]], Tuple[int, int]): """generate gridworld implemenation generates a dim by dim gridworld where the (0,0) is source every cell besides the source cell has probability of "prob" to be blocked if a cell is not blocked then it is randomly assigned a terrain type out of flat, hilly, and forest at the end a random not blocked cell will be chosen as the target NOTE: take of note of what is stored at each cell. The first value indicates what cell type it is and the second value indicates if the cell contains the target Args: dim (int) : the dimensions of the square grid prob (float): the probability of a square being blocked solvability (bool): returns a grid world that is either solvable or unsolvable depending on this param Returns: list[list[int]]: a dim by dim square grid with blocked and unblock squares Tuple[int, int]: a tuple containing the coordinates of target cell """ grid = np.array([[0.0] * dim for _ in range(dim)]) while True: for i in range(dim): for j in range(dim): p = random() if p >= prob: if is_terrain_enabled: t = randint(1,3) if t == 1: grid[i][j] = 0.8 # Terrain.FLAT elif t == 2: grid[i][j] = 0.5 # Terrain.HILLY elif t == 3: grid[i][j] = 0.2 # Terrain.FOREST else: grid[i][j] = 0.0 else: grid[i][j] = 1.0 # blocked cell # Place the source and target if source: grid[source[0]][source[1]] = 0.0 if target: grid[target[0]][target[1]] = 0.0 unblocked = [(i,j) for i in range(dim) for j in range(dim) if grid[i][j] != 1.0] # no unblocked cells if not unblocked: continue # choose q random source and target, note source and target can be the same cell if not source: source = choice(unblocked) if not target: source = choice(unblocked) # pretty_print_explored_region(grid) path, *_ = a_star(grid, source, target) # print(path) if solvable is None or solvable == bool(path): break return grid, source, target # Heuristics def manhattan_heuristic(x1: int, y1: int, x2: int, y2: int): return abs(x1 - x2) + abs(y1 - y2) # A* from collections import defaultdict import heapq from typing import Tuple, Callable, List, Dict import numpy as np def a_star( grid: List[List[float]], source: Tuple[int, int] = (0, 0), goal: Tuple[int, int] = None, h: Callable = manhattan_heuristic, W: int = 1, ) -> (List[Tuple[int, int]], int, List[Tuple[int,int]], int, int): """A* Implementation Used to find optimal shortest path from source to goal on a full gridworld (i.e with complete information of the block cells). Args: grid (2d int list): A square grid with 0 representing empty square and 1 representing a blocked square source (tuple) Optional: control where to start the A* search from goal (tuple): control where the target of the A* search algorithm h (func) Optional: heuristic function and the default heuristic function is the manhattan distance W (int) Optional: weight on the heuristic function Returns: list(tuple(int, int)): the path from the source node to the goal node which consists of coordinate points (i.e. x,y) in the form of tuples. int: the number of steps that need to taken from soure to goal (i.e. len(path) - 1 or 2(dim - 1)) list(tuple(int,int)): all visited squares from the source point. Contains int tuples in the form of (x,y). int: trajectory - num of times a node was popped from the fringe int: cells processed - num of times we check if a particular grid square was block or not """ if source == goal: return [source], 0, [source], 0, 0 g = defaultdict(lambda: float("inf")) # dictionary with key: (x,y) tuple and value: length of the shortest path from (0,0) to (x,y) g[source] = 0 # set distance to source 0 parent = { source: None } # dictionary with key: (x,y) tuple and value is a (x,y) tuple where the value point is the parent point of the key point in terms of path traversal pq = [(0, -g[source], source)] trajectory = cells_processed = 0 # statistics to keep track while A* runs while pq: _, _, curr = heapq.heappop(pq) # pop from priority queue # pretty_print_explored_region(grid, list(g.keys())) # uncomment if you want to see each step of A* trajectory += 1 if curr == goal: # check if we found our goal point break # generate children for dx, dy in [(0, 1), (-1, 0), (0, -1), (1, 0)]: # right down left up new_x, new_y = (curr[0] + dx, curr[1] + dy) # compute children # skip out of bounds or blocked children if not (0 <= new_x < len(grid) and 0 <= new_y < len(grid)) or grid[new_x][new_y] >= 1.0: continue children = (new_x, new_y) new_g= g[curr] + 1 # add + 1 to the real cost of children if ( children not in g or new_g < g[children] ): # only care about new undiscovered children or children with lower cost cells_processed += 1 g[children] = new_g # update cost parent[children] = curr # update parent of children to current h_value = h(*children, *goal) # calculate h value f = new_g + W * h_value # generate f(n') heapq.heappush( pq, (f, -new_g, children) ) # add children to priority queue | -new_g is added to break ties else: return [], float("inf"), list(g.keys()), trajectory, cells_processed # generate path traversal using parent dict path = [curr] while curr != source: curr = parent[curr] path.append(curr) path.reverse() # reverse the path so that it is the right order return path, g[goal], list(g.keys()), trajectory, cells_processed # Nicely prints out the grid # Testing use only. class colors: # PINK = '\033[95m' RED = '\033[91m' BLUE = '\033[94m' BOLD = '\033[1m' ENDC = '\033[0m' def pretty_print_explored_region(grid, visited=[], path=[]): color = '\33[102m' grid_copy = deepcopy(grid.tolist()) for x,y in visited: grid_copy[x][y] = colors.RED + str(grid[x][y]) + colors.ENDC for x,y in path: if type(grid_copy[x][y]) == str: grid_copy[x][y] = colors.PINK + str(grid[x][y]) + colors.ENDC else: grid_copy[x][y] = colors.BLUE + str(grid[x][y]) + colors.ENDC for i in range(len(grid)): for j in range(len(grid)): if type(grid_copy[i][j]) != str: if grid_copy[i][j] == 0.8: color = '\33[43m' elif grid_copy[i][j] == 0.5: color = '\33[102m' elif grid_copy[i][j] == 0.2: color = '\33[42m' elif grid_copy[i][j] == 1: color = '\33[100m' elif grid_copy[i][j] == 0: color = '\033[94m' elif grid_copy[i][j] == 2.0: color = '\033[104m' grid_copy[i][j] = color + str(grid_copy[i][j]) + colors.ENDC for row in grid_copy: for col in row: print(col, end=" ") # print each element separated by space print() # Add newline print("-------") # RED == visited # Purple == path that was also visited # Blue == path but was not visited (i.e. if agent knew the complete grid then it was have used the blue path) class CardinalDirection(Enum): NORTH = 0 EAST = 1 SOUTH = 2 WEST = 3 def __int__(self): return self.value def _get_cardinal_direction(x1, y1, x2, y2): if x1 > x2: return CardinalDirection.NORTH elif x1 < x2: return CardinalDirection.SOUTH elif y1 > y2: return CardinalDirection.WEST elif y1 < y2: return CardinalDirection.EAST else: raise Exception("Invalid cardinal direction")

import numpy as np ### Repeated A* - Remastered for P4 def repeated_a_star(complete_grid: List[List[int]], use_full_grid=False, h:Callable = manhattan_heuristic, source:Tuple[int,int] = (0,0), goal:Tuple[int,int] = None, W:int = 1) -> (List[List[int]], List[Tuple[int,int]], int, int, int, int): """Repeated A* Implementation Used to find optimal shortest path from source to goal on a full gridworld (i.e with complete information of the block cells). Args: complete_grid (2d int list): A square grid with 0 representing empty square and 1 representing a blocked square user_full_grid (bool) Optinal: False indicates that the agent should not be able to see the whole grid and True represents the agent has information of the entire grid h (func) Optional: heuristic function and the default heuristic function is the manhattan distance source (tuple) Optional: can control where to start the A* search from goal (tuple) Optional: can control where A* star tries to explore to Returns: list[list[int]]: the discovered grid after running repeated A* list[tuple[int, int]]: the path from the source node to the goal node which consists of coordinate points (i.e. x,y) in the form of tuples. int: the number of steps in the path from source to goal (i.e. len(path) - 1 or 2(dim - 1)) list[tuple[int,int]]: all visited squares from the source point. Contains int tuples in the form of (x,y). int: trajectory int: num of cells processed int: number of repeats in the repeated A* algorithm return grid, path_discovered_grid, len(path_discovered_grid)-1, list(g.keys()), trajectory, cells_processed, repeats """ original_source = source #we need to remember the original_source node bc in repeated A* source changes n, m = len(complete_grid) - 1, len(complete_grid[0]) - 1 # dimensions of grid goal = (n, m) # goal coord trajectory = cells_processed = repeats = 0 #statistics that is tracked during function call safe = set() if use_full_grid: # if agent is allowed knowledge of full grid grid = np.array(deepcopy(complete_grid)) else: # if agent has to discover grid = np.array([[0.5]*(m+1) for _ in range(n+1)]) grid[source[0]][source[1]] = 0.0 grid_states = [] actions = [] while source != goal: # execution step of repeated A* repeats += 1 path, *_ = a_star(grid, source, goal) if not path: # no path was found from original source to goal return grid, [], grid_states, actions, 0, list(safe), trajectory, cells_processed, repeats # iterator = iter(path) # next(iterator) for pos in path: # travel along the planned path x,y = pos if source != pos: source_neighbors = [source[0], source[1]] for dx, dy in [(0, 1), (-1, 0), (0, -1), (1, 0)]: # generate children new_x, new_y = (source[0] + dx, source[1] + dy) if 0 <= new_x <= n and 0 <= new_y <= m: source_neighbors.extend([new_x, new_y, grid[new_x][new_y]]) else: source_neighbors.extend([0,0,1]) grid_states.append([np.array(deepcopy(grid)), source_neighbors]) actions.append(_get_cardinal_direction(*source, *pos)) grid[x][y] = complete_grid[x][y] if grid[x][y] == 1.0: break safe.add(pos) # add to closed set trajectory += 1 source = pos # uncomment if we want agent to see in all 4 directions # for dx, dy in [(0, 1), (-1, 0), (0, -1), (1, 0)]: # generate children # new_x, new_y = [x + dx, y + dy]soru # if 0 <= new_x <= n and 0 <= new_y <= m: # grid[new_x][new_y] = complete_grid[new_x][new_y] # update knowledge of the grid world # pretty_print_explored_region(grid, list(safe)) if not use_full_grid: for i in range(len(grid)): for j in range(len(grid[0])): if not (i,j) in safe and grid[i][j] != 1.0: grid[i][j] = 2.0 path_discovered_grid, length_discovered_grid, visited_discovered_grid, _, _ = a_star(grid, original_source, target) # call regular A* to generate path from source to goal in final discovered grid world return grid, path_discovered_grid, grid_states, actions, len(path_discovered_grid)-1, list(safe), trajectory, cells_processed, repeats return grid, path_discovered_grid, grid_states, actions, len(path_discovered_grid) - 1, list(safe), trajectory, cells_processed, repeats

NUM_TRAINING_MAZES = 100 NUM_TESTING_MAZES = 100 MAZE_DIM = 50 DENSITY = 0.3 SOLVABILITY = True IS_TERRAIN_ENABLED = False source = (0,0) target = (MAZE_DIM-1, MAZE_DIM-1) # grid, _, _ = gen_gridworld(dim, density, solvability, is_terrain_enabled, source, target) # # pretty_print_explored_region(grid) # explored_grid, path, grid_states, actions, len_path, visited_cells, *_ = repeated_a_star(grid, True) # pretty_print_explored_region(explored_grid, visited_cells, path) # explored_grid, path, grid_states, actions, len_path, visited_cells, *_ = repeated_a_star(grid) # pretty_print_explored_region(explored_grid, visited_cells, path) # print(len(grid_states)) # print(len(actions))

def gen_grid_data(num_iter: int, data_format: int): all_grids = [] all_sources = [] all_actions = [] for i in range(num_iter): if i % 100 == 0: print("Maze", i) grid, _, _ = gen_gridworld(MAZE_DIM, DENSITY, SOLVABILITY, IS_TERRAIN_ENABLED, source, target) explored_grid, path, grid_states, actions, len_path, visited_cells, *_ = agent(grid, dataformat) grids, sources = zip(*grid_states) all_grids.extend(grids) all_sources.extend(sources) all_actions.extend(actions) return (np.array(all_grids), np.array(all_sources), np.array(all_actions)) def generate_confusion_matrix( data, labels ): mat = [ [ 0 for i in range(10) ] for j in range(10) ] out = model.predict(data) predictions = np.argmax( out , axis = 1 ) for i in range( data.shape[0] ): mat[ labels[i] ][ predictions[i] ] += 1 for i in range(10): print( "\t".join( [ str(c) for c in mat[i] ] ) )

# Full Dense Layer for P1's Agent # Model Creation # NUM_PROCESSES = 10 # PROCESS_DATA_LIMIT = 100 # BATCH_SIZE = 100 # NUM_TRAINING_MAZES = 1000 * NUM_PROCESSES # NUM_TESTING_MAZES = 100 # MAZE_DIM = 50 # DENSITY = 0.3 # SOLVABILITY = True # IS_TERRAIN_ENABLED = False # source = (0,0) # target = (MAZE_DIM-1, MAZE_DIM-1) # total_grid_states = [] # total_actions = [] # total_test_grid_states = [] # total_test_grid_actions = [] # def gen_grid_data(idx, num_iter): # all_grids = [] # all_sources = [] # all_actions = [] # for i in range(num_iter): # if i % 100 == 0: # print("Maze", i) # grid, _, _ = gen_gridworld(MAZE_DIM, DENSITY, SOLVABILITY, IS_TERRAIN_ENABLED, source, target) # explored_grid, path, grid_states, actions, len_path, visited_cells, *_ = repeated_a_star(grid) # grids, sources = zip(*grid_states) # all_grids.extend(grids) # all_sources.extend(sources) # all_actions.extend(actions) # return (np.array(all_grids), np.array(all_sources), np.array(all_actions)) # def gen_grid_data_train(idx=0): # return gen_grid_data(idx, 250) # def gen_grid_data_test(idx=0): # return gen_grid_data(idx, PROCESS_DATA_LIMIT) # arrs = [gen_grid_data_test()] # total_test_grids = np.array([x for sublist in arrs for x in sublist[0]]) # total_test_sources = np.array([x for sublist in arrs for x in sublist[1]]) # total_test_actions = np.array([x for sublist in arrs for x in sublist[2]]) ### BLOCK 4 ### # import tensorflow as tf # # To Enable GPU # # NOTE: Deepnote has no GPU available for free tier # physical_devices = tf.config.experimental.list_physical_devices('GPU') # print("NUM GPUs Available: ", len(physical_devices)) # tf.config.experimental.set_memory_growth(physical_devices[0], True) # def generate_confusion_matrix( data, labels ): # mat = [ [ 0 for i in range(4) ] for j in range(4) ] # print(np.shape(data[0])) # print(np.shape(data[1])) # out = model.predict(data) # predictions = np.argmax( out , axis = 1 ) # unique, counts = np.unique(predictions, return_counts=True) # print("predict", dict(zip(unique, counts))) # unique, counts = np.unique(np.array([int(x) for x in labels]), return_counts=True) # print("labels", dict(zip(unique, counts))) # for i in range( data[0].shape[0] ): # mat[ int(labels[i]) ][ predictions[i] ] += 1 # for i in range(4): # print( "\t".join( [ str(c) for c in mat[i] ] ) ) # grids_train = np.reshape(total_grids, (-1, MAZE_DIM, MAZE_DIM)) # sources_train = total_sources # output_train = tf.keras.utils.to_categorical( # np.array([int(x) for x in total_actions]), # 4, # ) # grids_test = np.array([x[0] for x in total_test_grid_states]) # grids_test = np.reshape(total_test_grids, (-1, MAZE_DIM, MAZE_DIM) ) # sources_test = total_test_sources # output_test = np.array([int(x) for x in total_test_actions]) # print("bottle 3") # Grid input # Models def generate_dense_model(): grid_input = tf.keras.layers.Input( shape = (MAZE_DIM, MAZE_DIM) ) flatten_grids = tf.keras.layers.Flatten()( grid_input ) # Source input source_input = tf.keras.layers.Input( shape = (14) ) # Full input grids_and_sources = tf.keras.layers.Concatenate()([flatten_grids, source_input]) dense_1 = tf.keras.layers.Dense( units = 2000, activation = tf.nn.relu )( grids_and_sources ) dense_2 = tf.keras.layers.Dense( units = 500, activation = tf.nn.relu )( dense_1 ) probabilities = tf.keras.layers.Dense( units = 4, activation = tf.nn.softmax )( dense_2 ) model = tf.keras.Model(inputs=[grid_input, source_input], outputs=probabilities) model.compile( optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'] ) print(model.summary()) return model def generate_cnn_model(): # Grid input grid_input = tf.keras.layers.Input( shape = (MAZE_DIM, MAZE_DIM) ) flatten_grids = tf.keras.layers.Flatten()( grid_input ) # Source input source_input = tf.keras.layers.Input( shape = (14) ) # Full input grids_and_sources = tf.keras.layers.Concatenate()([flatten_grids, source_input]) grids_and_sources = tf.expand_dims(grids_and_sources, axis = -1) grids_and_sources = tf.expand_dims(grids_and_sources, axis = -1) conv2d_1 = tf.keras.layers.Conv2D(filters=256,kernel_size=2,padding="same",activation="relu")( grids_and_sources ) maxpool2d_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_1 ) conv2d_2 = tf.keras.layers.Conv2D(filters=32,kernel_size=2,padding="same",activation="relu")( maxpool2d_1 ) maxpool2d_2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_2 ) conv2d_3 = tf.keras.layers.Conv2D(filters=4,kernel_size=2,padding="same",activation="relu")( maxpool2d_2 ) maxpool2d_3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_3 ) flatten = tf.keras.layers.Flatten()( maxpool2d_3 ) dense_1 = tf.keras.layers.Dense( units = 500, activation = tf.nn.relu )( flatten ) logits = tf.keras.layers.Dense( units = 4, activation = "softmax" )( dense_1 ) model = tf.keras.Model(inputs=[grid_input, source_input], outputs=logits) model.compile( optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'] ) print(model.summary()) return model # model = generate_cnn_model() # print("bottle 5") # model = tf.keras.models.load_model('./test_data/') # generate_confusion_matrix( [grids_test, sources_test], output_test ) # NUM_PROCESSES = 10 # PROCESS_DATA_LIMIT = 100 # BATCH_SIZE = 250 # from sklearn.utils import class_weight # NUM_TRAINING_MAZES = 1000 # total_grids = [] # total_sources = [] # total_actions = [] # for i in range(0, NUM_TRAINING_MAZES, BATCH_SIZE): # if i != 0: # model = tf.keras.models.load_model('./test_data/') # print(f"Training starting on maze {i}") # total_grids, total_sources, total_actions = gen_grid_data_train() # grids_train = np.reshape(total_grids, (-1, MAZE_DIM, MAZE_DIM)) # sources_train = total_sources # output_train = tf.keras.utils.to_categorical( # np.array([int(x) for x in total_actions]), # 4, # ) # # y_ints = [y.argmax() for y in output_train] # # class_weights = dict(enumerate(class_weight.compute_class_weight('balanced', classes=np.unique(y_ints), y=y_ints))) # north_multiplier = 3 # west_multiplier = 2 # class_weights = { # 0 : north_multiplier, # 1 : 1, # 2 : 1, # 3 : west_multiplier, # } # # multiplier = 0 if multiplier == 0 else multiplier - 1 # history = model.fit( [grids_train, sources_train], output_train, epochs = 10, validation_split=.1) # model.save('./test_data/') # generate_confusion_matrix( [grids_test, sources_test], output_test ) # model.save('./test_data/') # Dataset will be of shape (# of inputs, # rows, # cols)

# CNN for P1's Agent def p1_cnn_model(): # Grid input grid_input = tf.keras.layers.Input( shape = (MAZE_DIM, MAZE_DIM) ) flatten_grids = tf.keras.layers.Flatten()( grid_input ) # Source input source_input = tf.keras.layers.Input( shape = (2) ) # Full input grids_and_sources = tf.keras.layers.Concatenate()([flatten_grids, source_input]) #Conv2d layer requires minimum ndim of 4, so adding 2 more empty dims grids_and_sources = tf.expand_dims(grids_and_sources, axis = -1) grids_and_sources = tf.expand_dims(grids_and_sources, axis = -1) #Model conv2d_1 = tf.keras.layers.Conv2D(filters=256,kernel_size=2,padding="same",activation="relu")( grids_and_sources ) maxpool2d_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_1 ) conv2d_2 = tf.keras.layers.Conv2D(filters=32,kernel_size=2,padding="same",activation="relu")( maxpool2d_1 ) maxpool2d_2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_2 ) conv2d_3 = tf.keras.layers.Conv2D(filters=4,kernel_size=2,padding="same",activation="relu")( maxpool2d_2 ) maxpool2d_3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_3 ) flatten = tf.keras.layers.Flatten()( maxpool2d_3 ) dense_1 = tf.keras.layers.Dense( units = 500, activation = tf.nn.relu )( flatten ) logits = tf.keras.layers.Dense( units = 4, activation = "softmax" )( dense_1 ) model = tf.keras.Model(inputs=[grid_input, source_input], outputs=logits) model.compile( optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'] ) print(model.summary()) return model

# # Data # nn_path_data =[ # [172, 171, 161, 143, 150, 146, 125, 127, 176, 311, 137, 148, 179, 316, 143, 157, 221, 152, 213, 184, 156, 225, 148, 191, 173, 216, 158, 168, 164, 127, 167, 191, 174, 179, 171, 160, 194, 163, 154, 137, 165, 147, 179, 208, 181, 157, 199, 145, 168, 153, 188, 145, 183, 126, 135, 155, 189, 239, 164, 214, 152, 155, 160, 397, 177, 173, 145, 134, 157, 151, 171, 144, 219, 162, 224, 199, 155, 154, 156, 192, 183, 166, 157, 188, 166, 150, 177, 157, 162, 146, 155, 206, 133, 123, 162, 160, 157, 175, 176, 171], # [167, 156, 131, 212, 186, 129, 151, 179, 154, 136, 160, 172, 194, 128, 118, 163, 246, 129, 162, 136, 191, 144, 140, 191, 150, 148, 159, 163, 192, 141, 263, 149, 171, 139, 175, 189, 173, 129, 150, 137, 163, 182, 184, 127, 164, 114, 202, 171, 132, 141, 150, 190, 125, 172, 180, 200, 193, 138, 130, 142, 187, 173, 159, 146, 200, 193, 172, 127, 142, 167, 193, 198, 190, 163, 169, 143, 196, 198, 224, 276, 149, 174, 157, 141, 160, 175, 162, 134, 125, 211, 115, 152, 143, 233, 276, 184, 157, 155, 160, 136], # [143, 206, 150, 156, 189, 126, 149, 139, 158, 130, 160, 163, 203, 157, 139, 154, 201, 198, 220, 156, 186, 157, 127, 180, 178, 135, 176, 160, 180, 159, 173, 172, 180, 132, 184, 165, 133, 217, 227, 152, 189, 239, 166, 154, 163, 283, 142, 188, 151, 248, 152, 153, 163, 132, 147, 152, 221, 149, 165, 186, 151, 139, 193, 184, 181, 175, 210, 144, 139, 188, 144, 167, 157, 171, 175, 169, 180, 142, 125, 124, 191, 168, 186, 190, 203, 193, 183, 184, 176, 182, 168, 201, 147, 153, 162, 187, 162, 209, 141, 183], # [236, 178, 137, 121, 138, 160, 181, 136, 132, 139, 150, 168, 124, 211, 169, 182, 245, 165, 216, 275, 171, 195, 220, 170, 174, 165, 140, 138, 148, 125, 171, 179, 204, 216, 252, 172, 177, 133, 191, 131, 179, 157, 137, 256, 185, 174, 143, 149, 123, 151, 137, 211, 255, 135, 169, 208, 137, 180, 140, 195, 192, 227, 236, 133, 183, 212, 163, 131, 162, 142, 151, 150, 180, 149, 145, 124, 160, 198, 152, 165, 198, 156, 136, 273, 147, 145, 144, 174, 187, 148, 169, 148, 172, 192, 174, 155, 149, 136, 181, 157], # 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[159, 187, 206, 237, 147, 198, 224, 130, 167, 208, 240, 156, 216, 188, 141, 152, 131, 167, 146, 146, 178, 175, 139, 152, 183, 144, 248, 173, 132, 173, 180, 187, 148, 164, 159, 170, 139, 169, 231, 153, 156, 197, 204, 118, 190, 208, 137, 134, 188, 164, 132, 148, 124, 204, 235, 188, 144, 152, 188, 122, 177, 141, 143, 181, 168, 209, 191, 157, 152, 180, 184, 196, 132, 116, 123, 166, 184, 127, 164, 177, 148, 175, 135, 152, 141, 230, 159, 164, 188, 159, 143, 143, 141, 162, 183, 157, 147, 142, 148, 188], # ] # nn_traj_data = [ # [198, 206, 178, 158, 174, 172, 138, 136, 202, 416, 148, 168, 210, 560, 164, 184, 302, 202, 268, 210, 180, 298, 160, 238, 190, 356, 196, 184, 218, 140, 190, 254, 216, 226, 216, 190, 234, 188, 176, 146, 208, 168, 218, 276, 238, 184, 264, 162, 196, 190, 224, 170, 238, 134, 152, 174, 216, 350, 190, 246, 192, 174, 186, 682, 220, 204, 154, 144, 188, 176, 196, 162, 292, 190, 268, 228, 192, 216, 182, 232, 226, 194, 180, 224, 218, 166, 220, 184, 182, 158, 184, 310, 150, 130, 180, 184, 178, 188, 206, 194], # 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] # cnn_path_data =[ # [178, 182, 139, 146, 185, 129, 129, 130, 218, 292, 159, 205, 174, 295, 142, 156, 167, 152, 192, 196, 132, 213, 155, 190, 164, 188, 154, 199, 134, 117, 165, 187, 185, 136, 165, 158, 208, 149, 150, 140, 164, 147, 172, 198, 183, 131, 177, 147, 172, 159, 163, 136, 177, 126, 145, 149, 183, 214, 164, 205, 123, 157, 159, 214, 168, 168, 150, 134, 191, 152, 145, 145, 164, 216, 169, 245, 195, 208, 188, 365, 205, 217, 181, 186, 138, 151, 144, 149, 171, 146, 188, 175, 259, 122, 145, 204, 172, 183, 147, 274], # [169, 154, 181, 175, 162, 130, 173, 185, 161, 131, 175, 192, 134, 126, 139, 159, 164, 194, 162, 133, 177, 144, 138, 179, 149, 167, 173, 155, 160, 158, 247, 134, 166, 126, 171, 179, 156, 130, 134, 133, 180, 179, 165, 129, 130, 114, 196, 150, 163, 160, 146, 185, 128, 166, 194, 204, 187, 141, 130, 155, 185, 142, 168, 192, 158, 141, 143, 165, 135, 170, 270, 197, 178, 177, 149, 162, 199, 231, 147, 219, 150, 148, 157, 142, 150, 188, 162, 142, 142, 210, 115, 157, 153, 299, 173, 199, 144, 168, 143, 142], # ] # cnn_traj_data = [ # [198, 210, 152, 164, 210, 136, 140, 146, 268, 338, 170, 282, 192, 416, 158, 186, 208, 186, 212, 234, 150, 256, 178, 228, 180, 240, 180, 256, 150, 126, 192, 252, 216, 152, 200, 178, 246, 162, 162, 146, 186, 162, 206, 256, 216, 140, 238, 162, 190, 192, 180, 158, 224, 134, 168, 160, 202, 298, 184, 224, 136, 174, 186, 288, 190, 188, 154, 142, 234, 184, 164, 160, 190, 324, 198, 284, 256, 260, 208, 466, 248, 252, 230, 222, 152, 166, 160, 166, 190, 162, 216, 218, 338, 130, 158, 256, 212, 214, 152, 364], # [206, 176, 196, 198, 180, 138, 194, 210, 178, 146, 214, 214, 142, 132, 150, 188, 178, 236, 184, 140, 210, 152, 146, 214, 158, 198, 190, 182, 210, 178, 274, 148, 182, 134, 190, 222, 170, 138, 140, 146, 196, 214, 196, 138, 142, 120, 260, 172, 176, 186, 174, 214, 142, 186, 236, 258, 210, 164, 138, 180, 266, 166, 216, 246, 178, 154, 156, 192, 140, 182, 330, 226, 214, 210, 166, 188, 244, 292, 168, 292, 158, 160, 186, 150, 182, 204, 174, 152, 154, 232, 118, 182, 180, 396, 218, 244, 158, 202, 172, 154], # ] # cnn_time_data = [ # [3.1681611710009747, 3.4488704670002335, 2.5972534750035265, 2.39050128999952, 3.2114720739991753, 2.21953018700151, 2.2693315770011395, 2.1936211850043037, 4.004001472996606, 5.266598957001406, 2.719514608994359, 3.7566687229991658, 2.8973439629990025, 5.8524786829948425, 2.54256507899845, 3.073332918997039, 2.9624054540036013, 2.679070022000815, 3.3946765509972465, 3.393356473003223, 2.131488144004834, 3.7603600369984633, 2.657604979998723, 3.5237622869972256, 2.9966810870027984, 3.287517933000345, 2.8187359319999814, 3.647578831994906, 2.334226947998104, 2.425244407997525, 2.562130715996318, 3.546778969001025, 3.306786970999383, 2.3190064980008174, 2.7759447139978874, 2.748246379996999, 3.6393377860003966, 2.2729815380007494, 2.6706092660024296, 2.4872900230038795, 2.9862147189996904, 2.463295052999456, 3.235506531003921, 3.5093516210035887, 3.307985501996882, 2.0174351430032402, 2.876303734999965, 2.428748362995975, 3.375652690003335, 3.0004235390006215, 2.686729392000416, 2.7738840309975785, 3.5097064369983855, 2.193867797999701, 2.1587642910017166, 2.7350801730062813, 3.175484421000874, 3.7803448620034033, 2.961349263001466, 3.4810102789997472, 1.93788139600656, 3.102783415997692, 3.281143915002758, 4.547659856994869, 3.6292848410012084, 3.3342111149977427, 2.7366149719964596, 2.5005846380008734, 3.641561188000196, 2.79611100400507, 2.5080093610013137, 2.587433703993156, 3.0924017799989088, 4.064349804000813, 3.0644004969944945, 4.892293147000601, 4.038530581994564, 4.295947452999826, 3.74842912999884, 6.897448293995694, 3.745304677002423, 3.878832918999251, 3.138111257998389, 3.409589037000842, 2.3090972069985582, 2.6011260119994404, 2.420690259998082, 2.8477310970047256, 2.9397604209952988, 2.478690045994881, 3.305076871998608, 3.2803818749962375, 4.56525243500073, 2.1952713069986203, 2.466344464002759, 3.717819346995384, 2.593732893001288, 3.0792648589995224, 2.2692328090051888, 4.546211059001507], # 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] # a_path_data =[ # [172, 201, 134, 137, 153, 129, 129, 127, 174, 283, 159, 144, 174, 231, 142, 240, 164, 150, 184, 184, 153, 206, 144, 189, 161, 188, 147, 186, 134, 116, 162, 185, 175, 162, 161, 160, 207, 149, 147, 140, 162, 144, 172, 192, 229, 131, 177, 147, 150, 163, 162, 153, 176, 126, 144, 149, 183, 211, 164, 216, 129, 157, 196, 185, 166, 168, 150, 143, 177, 152, 144, 143, 179, 181, 155, 177, 177, 148, 162, 188, 150, 206, 157, 186, 193, 151, 144, 149, 174, 146, 166, 157, 221, 120, 145, 190, 152, 179, 154, 164], # [165, 155, 157, 191, 175, 129, 179, 163, 158, 129, 154, 186, 134, 123, 139, 148, 164, 192, 160, 133, 168, 144, 137, 198, 149, 172, 155, 155, 159, 158, 256, 136, 166, 124, 158, 176, 156, 130, 134, 136, 180, 176, 168, 128, 132, 114, 181, 171, 138, 155, 153, 165, 128, 164, 160, 191, 185, 137, 133, 155, 182, 146, 165, 181, 158, 140, 143, 159, 135, 168, 265, 194, 178, 150, 147, 162, 185, 207, 147, 253, 148, 148, 158, 142, 163, 166, 162, 140, 142, 204, 115, 199, 150, 285, 162, 183, 141, 168, 134, 142], # ] # a_traj_data = [ # [246, 315, 202, 204, 233, 185, 189, 185, 266, 426, 221, 213, 268, 427, 214, 384, 262, 245, 267, 281, 239, 300, 211, 302, 249, 315, 226, 310, 206, 178, 235, 317, 275, 269, 263, 262, 332, 206, 216, 208, 242, 203, 282, 319, 414, 182, 307, 210, 237, 248, 240, 231, 294, 180, 202, 221, 275, 361, 252, 327, 187, 242, 313, 315, 266, 253, 207, 208, 283, 232, 212, 209, 286, 316, 242, 325, 317, 246, 261, 304, 238, 337, 234, 298, 299, 233, 211, 237, 275, 225, 273, 244, 366, 168, 205, 324, 221, 287, 240, 237], # [261, 243, 241, 302, 266, 178, 276, 246, 245, 205, 230, 289, 190, 164, 203, 225, 251, 303, 231, 181, 260, 209, 200, 307, 215, 265, 233, 239, 260, 241, 363, 209, 256, 189, 248, 295, 222, 182, 185, 211, 273, 280, 274, 175, 186, 163, 309, 305, 198, 243, 256, 243, 200, 240, 274, 298, 279, 211, 201, 235, 326, 226, 292, 299, 254, 211, 215, 250, 186, 256, 449, 291, 287, 224, 208, 262, 312, 350, 217, 446, 213, 204, 260, 206, 249, 265, 223, 200, 197, 312, 156, 302, 239, 476, 263, 282, 201, 251, 186, 201], # ] # a_time_data = [ # [0.03764109400071902, 0.0665087639936246, 0.055336618002911564, 0.040562173002399504, 0.04211132999625988, 0.03701496499706991, 0.029527172999223694, 0.03046992699819384, 0.052500756006338634, 0.11247568399994634, 0.03238277600030415, 0.04376925800170284, 0.036870178002573084, 0.09187774400197668, 0.03047523599525448, 0.08022449100099038, 0.0431034740031464, 0.04383408099965891, 0.0401312269968912, 0.03998642199439928, 0.039533506002044305, 0.057588183000916615, 0.03489538400026504, 0.05877347600471694, 0.04503441700217081, 0.06283477300166851, 0.03367466400231933, 0.05791689600300742, 0.03354507999756606, 0.04060867600492202, 0.03304345200012904, 0.07326998899952741, 0.05628500199964037, 0.05682707199594006, 0.053689710002799984, 0.05700245999469189, 0.05808545000036247, 0.040780000999802724, 0.04950576599367196, 0.05084559500392061, 0.0429189580027014, 0.048003872005210724, 0.041323500001453795, 0.04930623900145292, 0.08477202700305497, 0.031486784006119706, 0.11072091100504622, 0.034518994005338755, 0.05179514999326784, 0.06562065599428024, 0.03846069800056284, 0.07542236299923388, 0.04859672699967632, 0.03368868500547251, 0.027400225997553207, 0.04908360500121489, 0.04184313500445569, 0.07303698199393693, 0.035634817999380175, 0.10317792299611028, 0.03038611500232946, 0.034473084000637755, 0.05132676699577132, 0.06860959399637068, 0.10102555200137431, 0.03964684499806026, 0.033419158004107885, 0.07612029400479514, 0.05185671000072034, 0.06676756500382908, 0.030711729006725363, 0.03168183499656152, 0.03670132899424061, 0.07147242700011702, 0.041541025006154086, 0.07393235699419165, 0.06053043800056912, 0.037619652997818775, 0.05440364500100259, 0.06293175499740755, 0.05568182199931471, 0.06096842400438618, 0.03585370800283272, 0.048104785004397854, 0.07540274199709529, 0.0364063380038715, 0.039604245001100935, 0.03865141999995103, 0.06167639000341296, 0.04609003199584549, 0.038729963001969736, 0.05075645900069503, 0.07289155200123787, 0.03347561300324742, 0.02754861499852268, 0.047877056000288576, 0.0515510799959884, 0.08050083499983884, 0.04470267800206784, 0.04011809099756647], # [0.06059766700491309, 0.044321524001134094, 0.044852432001789566, 0.05600145799689926, 0.04371619399898918, 0.034987535000254866, 0.04593473400018411, 0.035924358999182004, 0.05700145500304643, 0.048054200000478886, 0.036137909002718516, 0.052905071002896875, 0.036891600997478236, 0.024210422998294234, 0.030869342997903004, 0.03456927899969742, 0.05486399600340519, 0.049145509001391474, 0.0292262840011972, 0.031160965001618024, 0.04314926200459013, 0.03736786099761957, 0.03871891900053015, 0.058568262997141574, 0.034802846996171866, 0.06539985199924558, 0.03965758399863262, 0.06704498599719955, 0.05524629499996081, 0.040190864994656295, 0.07270387899916386, 0.0407109540028614, 0.04606305899505969, 0.03648358999635093, 0.04739266200340353, 0.056367481003690045, 0.0343653070012806, 0.028153966006357223, 0.031423766995430924, 0.040090016002068296, 0.04929436400561826, 0.04215431299962802, 0.0690132480012835, 0.03548841000156244, 0.034863436994783115, 0.029178277996834368, 0.09049790399876656, 0.07526266600325471, 0.031259764000424184, 0.03934472100081621, 0.06213217500044266, 0.04164046500227414, 0.04282726800011005, 0.03346603699901607, 0.0558953189975, 0.07174053600465413, 0.04245580400311155, 0.050032202001602855, 0.03799430999788456, 0.044824462005635723, 0.11715058999834582, 0.06697170199913671, 0.14550606600096216, 0.04877648500405485, 0.0633019150045584, 0.03407352700014599, 0.03742479899665341, 0.038927824003621936, 0.042967663997842465, 0.0609712329969625, 0.09092714000144042, 0.04700433099787915, 0.05556646500190254, 0.0371584710010211, 0.02929646100528771, 0.04669775700313039, 0.0539834939991124, 0.05742871400434524, 0.03565712099953089, 0.07697771699895384, 0.029064542999549303, 0.02602607200242346, 0.03956666799786035, 0.040727680003328715, 0.038130745000671595, 0.04621806100476533, 0.02929288100131089, 0.06483432200184325, 0.028797492996091023, 0.05589781500020763, 0.02958510299504269, 0.058332075001089834, 0.11833356100396486, 0.16752738200011663, 0.05765397800132632, 0.05956849700305611, 0.03988447099982295, 0.05109297700255411, 0.028284004001761787, 0.030339450000610668], # ] # # Remaining data not yet pput into arrays (groups of 100) # NN Traj [158, 286, 188, 174, 242, 140, 156, 152, 180, 142, 192, 196, 258, 178, 158, 176, 266, 288, 308, 182, 226, 210, 144, 232, 230, 156, 236, 178, 224, 188, 214, 216, 232, 152, 228, 194, 144, 302, 304, 190, 250, 340, 196, 156, 184, 360, 152, 248, 172, 350, 172, 194, 204, 144, 160, 170, 288, 168, 208, 218, 184, 154, 226, 206, 210, 232, 280, 160, 158, 232, 166, 204, 186, 218, 246, 234, 206, 164, 132, 140, 258, 218, 220, 256, 232, 288, 216, 274, 208, 242, 190, 236, 174, 180, 172, 228, 198, 254, 164, 268] # NN Time [1.3212577890008106, 1.9925428720016498, 1.3859341220013448, 1.4921062669964158, 1.965525075996993, 1.0918242190018645, 1.383629554999061, 1.2273182930002804, 1.6578422710008454, 1.0917626789960195, 1.6642442839947762, 1.5164902140022605, 1.8275394200027222, 1.401741535999463, 1.289632284999243, 1.3692463170009432, 2.0517160320014227, 2.1291743889960344, 2.3463153379998403, 1.2555686500054435, 1.8674906399974134, 1.4713658559994656, 1.2121894880037871, 1.6551104230020428, 1.7551386889972491, 1.238382997995359, 1.9709356849998585, 1.3084131499999785, 1.6703547579963924, 1.5418989840036375, 1.5615753020028933, 1.7052438819955569, 1.6515617219993146, 1.2367673230037326, 1.5349027129996102, 1.4816381900018314, 1.1258776110043982, 2.4193257439983427, 2.3871968629973708, 2.6267324489963357, 2.7794182319994434, 4.116087060996506, 3.523471344000427, 1.7980555339963757, 1.4649449959979393, 2.96255233699776, 1.3810303479986032, 2.073908033002226, 1.392418313997041, 2.508210179003072, 1.7352602049941197, 1.5681698729968048, 1.9273307109979214, 1.1843873539983178, 1.56123356900207, 1.8738530909977271, 2.1801255000027595, 1.3677090589990257, 1.8160756040015258, 1.751122685993323, 1.1401434490035172, 1.197604050001246, 1.7129510140002822, 1.5621954550006194, 1.5126225510030054, 1.6719115700034308, 1.8650925140027539, 1.3389352570011397, 1.1960232900019037, 1.8027359010011423, 1.2123910179943778, 1.6604682120014331, 1.36814405000041, 1.5568391190026887, 1.7133855099964421, 1.4436364029970719, 1.6422913339993102, 1.2569241259989212, 1.0541661610041047, 1.0454005009960383, 1.5958859980019042, 1.6708123909993446, 1.6902598020024016, 1.7733599159982987, 1.833881590006058, 2.0393629140016856, 1.719179918000009, 2.0140843690023758, 1.7394078900033492, 1.8313406050001504, 1.7330921989996568, 1.8716963350016158, 1.3781554410015815, 1.48177477200079, 1.36401301099977, 1.9833854219978093, 1.428101715995581, 1.6270873530011158, 1.2846593180001946, 1.8905200650042389] # CNN Path [155, 186, 156, 153, 189, 128, 134, 122, 167, 135, 155, 186, 193, 150, 133, 141, 162, 161, 234, 156, 173, 127, 115, 147, 161, 137, 193, 158, 152, 168, 184, 165, 173, 131, 135, 162, 128, 145, 215, 156, 151, 153, 161, 154, 177, 218, 154, 187, 155, 265, 147, 142, 163, 123, 145, 141, 198, 184, 185, 180, 149, 138, 185, 165, 130, 128, 204, 154, 150, 198, 135, 163, 147, 159, 147, 190, 165, 152, 133, 123, 187, 156, 212, 191, 174, 200, 191, 195, 226, 161, 168, 186, 136, 232, 152, 174, 157, 209, 146, 151] # CNN Traj [182, 238, 196, 170, 246, 144, 138, 128, 194, 142, 170, 228, 218, 168, 152, 156, 180, 194, 312, 174, 202, 142, 122, 154, 208, 154, 220, 176, 174, 186, 232, 190, 210, 144, 150, 184, 132, 160, 258, 174, 178, 172, 188, 156, 196, 258, 178, 244, 172, 358, 158, 164, 216, 130, 158, 156, 228, 224, 214, 200, 170, 150, 214, 184, 144, 138, 250, 202, 178, 226, 148, 176, 164, 178, 170, 252, 186, 178, 138, 130, 238, 176, 284, 246, 184, 246, 232, 266, 286, 184, 196, 216, 142, 300, 194, 198, 176, 248, 176, 162] # CNN Time [2.711082063993672, 3.360159816002124, 2.4103589779988397, 2.814428728997882, 3.9095874690028722, 2.343037851002009, 2.5263848089962266, 1.9235784070042428, 2.7977815010017366, 2.1468019550011377, 2.9434080020000692, 3.6278398399954312, 3.4232137280050665, 2.75045240700274, 2.039091312995879, 2.5355182910061558, 3.0564265240027453, 3.133683671003382, 4.309712808004406, 2.823043731004873, 3.2579220879997592, 2.2821742500018445, 2.0027810159954242, 2.3793237799982307, 2.9308623199976864, 2.175165319000371, 3.5040336800011573, 2.508222181000747, 2.4399698410052224, 2.951628137998341, 3.430839189000835, 2.9938548259960953, 3.3077243320003618, 2.3336603689967887, 2.30668538199825, 2.82677543599857, 1.9464514600040275, 2.4404622389993165, 7.249775797004986, 5.745624082999711, 5.3269884979963535, 8.641017690999433, 7.737131839996437, 2.895719125997857, 3.799083691003034, 4.672995979999541, 2.816895960997499, 3.9647072269945056, 2.9998880150014884, 6.232357878005132, 3.347415946998808, 3.205842149000091, 2.9803414500056533, 1.9407362510028179, 2.9318480500005535, 2.7663469029939733, 3.44031678999454, 3.7135396090016, 3.7496643609993043, 3.2510515229951125, 2.5566094040041207, 2.381697887998598, 3.298104030000104, 2.65121609599737, 2.169672404997982, 1.950294741000107, 3.5918463979978696, 2.581729618999816, 2.8209513889960363, 3.49545944500278, 2.094565125000372, 2.8403869599933387, 2.3321344229989336, 2.8226805529993726, 2.587849434996315, 3.3409201279937406, 2.9403476399966166, 2.4944126210029935, 2.092010244996345, 1.954739177999727, 3.301532457000576, 2.7501605249999557, 3.8731951910012867, 3.2537436069978867, 3.0512338699991233, 3.6181718919979176, 3.409836382998037, 3.5232774070027517, 4.1151843030020245, 2.8276591839967296, 2.934140084995306, 3.068190054000297, 2.4336851369953365, 4.413591144999373, 2.8378188950009644, 3.071988056995906, 2.6329255890013883, 3.6279318000015337, 2.5155894560011802, 2.8506642369975452] # A Path [148, 185, 157, 159, 155, 128, 134, 122, 170, 135, 163, 177, 190, 153, 133, 141, 162, 159, 228, 155, 162, 138, 115, 147, 161, 137, 194, 156, 210, 160, 161, 166, 167, 139, 135, 161, 128, 145, 210, 156, 151, 152, 164, 154, 164, 212, 186, 168, 167, 241, 147, 139, 157, 144, 151, 161, 197, 179, 161, 180, 145, 138, 183, 175, 130, 128, 201, 151, 139, 185, 132, 163, 134, 145, 147, 205, 165, 149, 126, 123, 158, 167, 227, 183, 173, 200, 175, 186, 222, 156, 165, 127, 136, 144, 167, 167, 156, 208, 141, 151] # A Traj [219, 323, 256, 248, 237, 190, 192, 158, 260, 177, 261, 284, 286, 239, 202, 222, 255, 273, 383, 223, 272, 230, 171, 204, 273, 200, 319, 228, 365, 249, 228, 263, 263, 227, 201, 243, 165, 209, 350, 232, 236, 240, 262, 200, 253, 338, 283, 265, 250, 410, 217, 211, 247, 223, 226, 250, 306, 274, 264, 269, 209, 200, 279, 262, 192, 172, 324, 239, 215, 291, 187, 250, 210, 216, 227, 352, 252, 221, 174, 170, 239, 283, 393, 297, 247, 332, 290, 344, 401, 251, 271, 185, 192, 232, 256, 254, 231, 315, 217, 237] # A Time [0.04326045200286899, 0.051850573006959166, 0.05430974200135097, 0.05303594500583131, 0.04570116399554536, 0.039044782002747525, 0.03518680100387428, 0.02227607099484885, 0.06342742999549955, 0.035134876998199616, 0.04856938600278227, 0.06087633300194284, 0.04494999199960148, 0.042730002001917455, 0.04018744900531601, 0.040613445002236404, 0.04560205199959455, 0.06288264899922069, 0.08604311799717834, 0.031193105998681858, 0.061960535997059196, 0.05067266499827383, 0.034087302003172226, 0.039914455999678466, 0.06953652700030943, 0.038544707000255585, 0.062337283001397736, 0.031694805998995434, 0.07207159799872898, 0.04633512000145856, 0.036190220998832956, 0.05062284800078487, 0.04506821499671787, 0.03770229600195307, 0.03713996899750782, 0.040979500001412816, 0.017390916000294965, 0.03442163900035666, 0.08519965400046203, 0.06763657999545103, 0.11181949300225824, 0.1360999480020837, 0.08034329499787418, 0.031582003000949044, 0.05417806899640709, 0.10522602999844821, 0.06454800099891145, 0.042256225999153685, 0.046528282000508625, 0.0780488050004351, 0.05487411700596567, 0.05783274599525612, 0.05470834100560751, 0.03906335899955593, 0.057480971998302266, 0.03930255400337046, 0.04133783899305854, 0.04202608700143173, 0.0506966890025069, 0.05153069700463675, 0.03172251100477297, 0.039150333002908155, 0.039017419003357645, 0.061327715004154015, 0.030289039001218043, 0.024384370997722726, 0.056557789001089986, 0.05720518199814251, 0.0410596829970018, 0.07504594799684128, 0.026770820004458074, 0.04000819499924546, 0.04284277799888514, 0.041523981002683286, 0.03336424199369503, 0.06809560300462181, 0.04300578399852384, 0.0521691899994039, 0.030581840001104865, 0.027566729004320223, 0.039313431007030886, 0.046262224997917656, 0.08027823999873362, 0.05241790999571094, 0.04242302400234621, 0.053181338000285905, 0.06403886499902, 0.06517877699661767, 0.07930802500050049, 0.03836063499329612, 0.05043060699972557, 0.029304352996405214, 0.028831475996412337, 0.04270634899876313, 0.04144349200214492, 0.04035905199998524, 0.03629380899656098, 0.053951046997099183, 0.030079632000706624, 0.03998750800383277] # starting iteration 3 # NN Traj [316, 242, 144, 130, 148, 198, 204, 148, 148, 156, 180, 184, 130, 308, 194, 242, 374, 188, 304, 382, 212, 244, 290, 212, 192, 206, 156, 154, 166, 132, 194, 240, 264, 268, 366, 200, 214, 150, 252, 144, 204, 184, 146, 368, 254, 204, 152, 178, 126, 172, 148, 254, 328, 148, 194, 278, 150, 224, 160, 258, 248, 304, 324, 154, 242, 262, 192, 152, 198, 168, 170, 178, 254, 190, 162, 140, 196, 252, 168, 192, 258, 180, 164, 370, 178, 168, 150, 236, 210, 168, 182, 168, 238, 268, 206, 174, 190, 154, 220, 184] # NN Time [2.3131831509963376, 1.9592696899999282, 1.1757495279962313, 1.1162934629974188, 1.2056505209984607, 1.628381795999303, 1.6193115950009087, 1.184142060999875, 1.1214932040020358, 1.1213788320019376, 1.3345970379959908, 1.5084329419987625, 1.0881288469972787, 2.5816097439965233, 1.466197301997454, 1.9190272750056465, 2.5244659680029145, 1.577415909996489, 2.190823803000967, 3.0233920050013694, 1.7538702970050508, 1.8991334689999348, 2.1044306859985227, 1.4652353779965779, 1.6574595999991288, 1.6027719570047338, 1.0961469290050445, 1.2187497180057107, 1.3944048289995408, 1.1185819960010122, 1.540080181999656, 1.7789756970014423, 1.9221053319997736, 1.9515726099998574, 2.581804976995045, 1.6516621420014417, 1.6605321960014408, 1.1264536750022671, 1.853592708997894, 1.2055239629989956, 1.463603886004421, 1.525715436997416, 1.3319383150010253, 2.6544540179966134, 2.0457256009976845, 1.5161012000025949, 1.4016927749980823, 1.4130931040053838, 1.005016158997023, 1.3371255149977515, 1.1590161040003295, 1.91730419400119, 2.4123229239994544, 1.0638457710010698, 1.5192504820006434, 2.149744362999627, 1.270587869999872, 1.9520225710002705, 1.1762785620012437, 1.9623558989987941, 1.745537955997861, 2.3166746979986783, 2.115075314002752, 1.203904574002081, 1.89231960199686, 1.7100835259989253, 1.543843484003446, 1.1807803330011666, 1.5204758899999433, 1.3458891150003183, 1.2529576779998024, 1.359813001996372, 1.7120116930018412, 1.4216985430030036, 1.3822036589990603, 1.2067782660014927, 1.4894425560050877, 1.7831931350010564, 1.3058568600026774, 1.3054844119978952, 2.059465950995218, 1.4581472459976794, 1.270023143995786, 2.62894125800085, 1.3782471529993927, 1.3241366399961407, 1.2472128329973202, 1.5665804310046951, 1.6826258090004558, 1.189605464001943, 1.4288445909987786, 1.5222388010006398, 1.7531571130020893, 1.815441214996099, 1.4357464810018428, 1.3139854370019748, 1.5090739529987331, 1.1563964579981985, 1.6351650880023954, 1.614636698999675] # CNN Path [260, 192, 135, 121, 147, 180, 170, 136, 142, 139, 138, 167, 124, 213, 228, 232, 202, 233, 176, 267, 188, 194, 320, 176, 158, 142, 142, 143, 149, 146, 169, 129, 194, 171, 252, 138, 172, 130, 162, 130, 190, 149, 137, 251, 259, 150, 158, 142, 120, 152, 138, 200, 214, 134, 148, 220, 137, 183, 153, 197, 152, 176, 182, 139, 132, 193, 150, 137, 147, 136, 150, 133, 197, 272, 265, 123, 164, 175, 126, 165, 219, 155, 131, 262, 137, 141, 142, 219, 174, 148, 164, 139, 170, 150, 161, 158, 133, 131, 177, 161] # CNN Traj [342, 228, 154, 130, 154, 216, 192, 146, 156, 150, 154, 184, 130, 270, 288, 282, 302, 308, 206, 330, 228, 244, 516, 206, 176, 156, 158, 160, 166, 156, 186, 142, 224, 190, 344, 152, 202, 136, 200, 142, 234, 182, 146, 354, 364, 176, 174, 160, 122, 170, 150, 232, 256, 144, 168, 258, 152, 216, 172, 230, 166, 218, 202, 164, 148, 228, 160, 158, 174, 154, 160, 140, 244, 362, 354, 132, 186, 218, 132, 180, 296, 174, 138, 334, 172, 160, 146, 308, 182, 168, 174, 154, 206, 176, 182, 176, 146, 146, 202, 178] # CNN Time [4.945114360998559, 3.3420685659948504, 2.351106369002082, 2.0140302859945223, 2.2618696770005045, 3.3318038859943044, 2.9137950820004335, 2.2930109279986937, 2.416666309996799, 2.1032558379956754, 2.400186106999172, 3.0540676079981495, 1.9901796339981956, 4.049380112999643, 4.105204333005531, 4.355358982000325, 3.895965024996258, 4.640226133997203, 2.9836677450002753, 5.148707915002888, 3.4758050960008404, 3.5822699530035607, 5.769960819998232, 3.045340117998421, 2.765636860000086, 2.522148705997097, 2.52376502900006, 2.425705886002106, 2.7079717130036443, 2.4153163349983515, 2.866817722999258, 2.2354098970026826, 3.408465298001829, 2.8238023110025097, 4.924985402998573, 2.415222811003332, 2.899627496000903, 2.254958543002431, 2.996228123003675, 2.2130638179951347, 3.364184245998331, 2.618534016000922, 2.434872620004171, 5.046626000999822, 5.355156862999138, 2.6763259959989227, 2.737001096997119, 2.3698084880015813, 1.7947202659997856, 2.6278330109998933, 2.2366199679963756, 3.7698755279998295, 4.018575535999844, 2.0024352010004804, 2.3574645780026913, 4.100198995998653, 2.2973408230027417, 3.713311583000177, 2.720891043994925, 3.9146145330014406, 2.378712023004482, 3.0677492530012387, 2.9915382440012763, 2.2120918209984666, 2.3063199410025845, 3.295006487998762, 2.5729798860047595, 2.084448137997242, 2.584196632000385, 2.4350837120000506, 2.4216039719976834, 2.2817018979985733, 3.85980581700278, 4.994538202001422, 4.647876504001033, 2.0678355990021373, 2.8701243640025496, 3.372562086005928, 2.1334525019992725, 2.4640426599944476, 4.0792334619982284, 3.0081565590007813, 2.0114090879942523, 4.944621315000404, 2.532898714998737, 2.4456944779958576, 2.198961814996437, 3.749500978003198, 2.7139484970030026, 2.5658725239991327, 2.8190564640026423, 2.2420506100024795, 3.3405181810012436, 2.5494875040021725, 2.5462262720029685, 2.7616324390037335, 2.3030392820000998, 2.2553941719961585, 2.986115419997077, 2.7509792650016607] # A Path [244, 189, 132, 121, 139, 163, 169, 136, 142, 139, 138, 177, 124, 204, 193, 230, 199, 217, 179, 223, 186, 178, 193, 166, 159, 142, 137, 137, 149, 146, 169, 129, 194, 171, 226, 137, 156, 132, 175, 130, 193, 143, 137, 221, 157, 138, 157, 188, 120, 152, 136, 199, 203, 134, 172, 198, 134, 176, 141, 196, 152, 173, 190, 134, 147, 190, 165, 126, 145, 136, 148, 144, 188, 204, 250, 123, 141, 173, 126, 165, 184, 155, 127, 231, 148, 140, 142, 154, 176, 148, 165, 167, 164, 146, 160, 159, 153, 131, 169, 162] # A Traj [428, 304, 201, 177, 197, 281, 253, 196, 216, 195, 206, 272, 176, 332, 307, 373, 367, 363, 271, 389, 304, 294, 333, 266, 245, 222, 199, 208, 225, 214, 246, 197, 295, 248, 401, 199, 240, 185, 299, 186, 307, 218, 205, 388, 261, 195, 227, 335, 147, 231, 196, 316, 324, 191, 251, 314, 202, 289, 202, 306, 217, 289, 299, 195, 231, 296, 248, 193, 234, 215, 208, 215, 308, 336, 420, 185, 216, 287, 172, 228, 308, 238, 176, 378, 259, 212, 195, 232, 245, 227, 242, 261, 276, 225, 237, 249, 235, 203, 265, 249] # A Time [0.08892450200073654, 0.04267270600394113, 0.03693932099849917, 0.03570451599807711, 0.028758701002516318, 0.055100133999076206, 0.0348225839989027, 0.030272582996985875, 0.03910133199678967, 0.03030911899986677, 0.0352981020041625, 0.040246546996058896, 0.031535709000309, 0.05188021000503795, 0.054147657996509224, 0.07277082899963716, 0.06854295400262345, 0.07194811500085052, 0.04628036900248844, 0.07148547999531729, 0.06051465299970005, 0.05260770599852549, 0.05164230200171005, 0.0488976909982739, 0.04197708900028374, 0.04144760000053793, 0.0310659519964247, 0.035501523001585156, 0.03954965100274421, 0.0472352819997468, 0.03594075000000885, 0.043282657003146596, 0.041589045998989604, 0.07888542299770052, 0.07613752400357043, 0.03402561000257265, 0.03721763499925146, 0.026358759998402093, 0.049896197000634857, 0.02677972499805037, 0.057379759004106745, 0.042101825994905084, 0.03989081600593636, 0.07822594600293087, 0.042355479003163055, 0.032610895003017504, 0.032807711002533324, 0.07284381700446829, 0.02887890899728518, 0.05217055500543211, 0.03990672800136963, 0.04818862899992382, 0.059414356997876894, 0.03587033299845643, 0.05952012199850287, 0.05879090500093298, 0.04983734000416007, 0.054740993000450544, 0.03298489000007976, 0.04058156700193649, 0.029498951997084077, 0.056697539999731816, 0.06145005200232845, 0.034602992003783584, 0.04549926899926504, 0.03797245500027202, 0.040333171004022006, 0.03361676799977431, 0.04597364999790443, 0.04196097499516327, 0.034056587996019516, 0.04042021700297482, 0.05231480499787722, 0.058599515999958385, 0.0799129700026242, 0.03608046600129455, 0.04029892299877247, 0.05233828999917023, 0.03652066300128354, 0.02799215000413824, 0.07322925300104544, 0.04320804900635267, 0.029960290994495153, 0.06793834899872309, 0.05853194300289033, 0.03499812399968505, 0.03141881200281205, 0.04696276300091995, 0.03242510099516949, 0.03809566899872152, 0.03656515300099272, 0.04940078499930678, 0.046909805001632776, 0.04238029699627077, 0.0328849369980162, 0.04852876499353442, 0.040025950998824555, 0.05995137900026748, 0.04450982499838574, 0.057083924002654385] # starting iteration 4 # NN Traj [348, 176, 150, 256, 316, 166, 188, 260, 158, 368, 196, 216, 320, 336, 164, 304, 256, 282, 420, 204, 202, 158, 180, 248, 254, 186, 206, 232, 158, 244, 150, 162, 160, 198, 252, 232, 160, 130, 256, 200, 374, 212, 122, 250, 148, 152, 178, 198, 272, 208, 210, 164, 206, 182, 200, 264, 160, 220, 166, 148, 228, 182, 266, 208, 156, 166, 242, 180, 338, 136, 152, 178, 204, 226, 168, 126, 204, 186, 144, 174, 188, 254, 208, 202, 314, 150, 172, 212, 178, 208, 402, 152, 238, 196, 246, 178, 312, 154, 170, 230] # NN Time [2.2027992329967674, 1.3496584290041937, 1.2511746649979614, 1.9258080270010396, 2.297494682003162, 1.4011027509986889, 1.4612887149996823, 1.8652982280036667, 1.1928860250045545, 2.2067909889956354, 1.6024519569982658, 1.6348459639993962, 2.3107727609967696, 2.2126650779973716, 1.206642662997183, 2.2855880289935158, 2.0508350759992027, 1.9387622610011022, 2.7747432500036666, 1.536026983994816, 1.4865079990049708, 1.2625442179996753, 1.4433376750021125, 1.7465356449974934, 1.6743906060000882, 1.5666758549996302, 1.7048464360050275, 1.9297468770018895, 1.2783846980019007, 2.063063664994843, 1.7902150690060807, 1.48318125300284, 1.280140492002829, 1.6232332970030257, 1.745448296998802, 2.138889529000153, 1.3564424059950397, 0.941764121002052, 2.0047557989964844, 1.6171188630032702, 2.3869185160001507, 1.9074787249992369, 1.0690885690055438, 2.0264085900053033, 1.300367825999274, 1.1253905150006176, 1.4126482819992816, 1.3693520669985446, 2.0307225479991757, 1.5534390920001897, 1.616009170000325, 1.3197858780040406, 1.6217127839991008, 1.3694937489999575, 1.6088452300027711, 1.9757093849984813, 1.3265166159981163, 1.74275315500563, 1.3771848640026292, 1.1222375700017437, 1.8447507720047724, 1.4301859229963156, 1.9911471720042755, 1.4906387540031574, 1.4893984380032634, 1.306276584000443, 1.6511586339984206, 1.0509032679983648, 2.430802507995395, 1.0685883480036864, 1.1775424309962546, 1.2918931480016909, 1.6031489229935687, 1.8234451669995906, 1.3474417820034432, 0.9648501389965531, 1.6704511479983921, 1.4652484999969602, 1.2575578830001177, 1.3079627839979366, 1.5615625799982809, 1.8951313349971315, 1.4507103740033926, 1.5670142020026105, 2.312560852995375, 1.1428042599945911, 1.4247600620001322, 1.7924320040037856, 1.4753199100014172, 1.384682238996902, 2.7915558620006777, 1.2578519099988625, 1.8367122230047244, 1.5772400779969757, 1.9524013420013944, 1.4599449800007278, 2.243018419998407, 1.1355832609988283, 1.4245310400001472, 1.6082047180025256] # CNN Path [230, 152, 144, 229, 209, 150, 170, 219, 131, 199, 171, 291, 216, 280, 144, 162, 202, 286, 260, 183, 154, 140, 157, 154, 200, 178, 162, 150, 204, 207, 132, 158, 145, 157, 212, 201, 149, 129, 160, 179, 240, 171, 116, 170, 139, 135, 145, 159, 202, 159, 159, 146, 149, 237, 184, 212, 135, 258, 152, 158, 156, 151, 193, 186, 135, 266, 250, 154, 231, 131, 135, 131, 166, 129, 145, 131, 174, 179, 131, 143, 162, 164, 159, 215, 289, 152, 153, 179, 142, 168, 246, 149, 165, 141, 229, 160, 174, 136, 162, 155] # CNN Traj [290, 168, 168, 300, 254, 164, 222, 270, 134, 280, 194, 330, 264, 346, 160, 178, 256, 382, 344, 236, 178, 158, 178, 172, 232, 206, 184, 166, 242, 254, 148, 174, 156, 184, 274, 250, 172, 144, 186, 208, 296, 200, 130, 204, 154, 152, 178, 184, 240, 192, 202, 162, 156, 326, 206, 264, 150, 286, 186, 182, 176, 178, 230, 248, 148, 324, 292, 172, 264, 146, 148, 138, 198, 136, 162, 138, 226, 238, 144, 152, 186, 192, 190, 280, 338, 170, 174, 210, 164, 214, 288, 170, 192, 162, 286, 178, 210, 150, 176, 218] # CNN Time [4.121950959000969, 2.4739014669976314, 2.254333705001045, 4.264367643998412, 3.605567761005659, 2.5821802359932917, 3.083163796000008, 3.6505684449948603, 2.0159359399985988, 3.6912219480000203, 2.977504452006542, 5.107955277999281, 3.8440609450044576, 4.810199710998859, 2.375150357001985, 2.8396554209975875, 3.7134365969977807, 5.374689226002374, 5.293794142002298, 3.1462977999981376, 2.9073227379994933, 2.433296903000155, 2.954053813999053, 2.777429623005446, 3.6533715529949404, 3.216809267003555, 3.2877588720002677, 2.3821069140030886, 3.796705814995221, 3.7881551359969308, 2.448793587995169, 2.610754826993798, 2.741181309997046, 2.7758836049979436, 4.7229290879986365, 3.854869475006126, 2.9167685770007665, 2.20317499399971, 3.05708192900056, 3.2668319509975845, 4.540833936000126, 3.1070797560023493, 2.1224256900022738, 2.8935779680032283, 2.6327961729984963, 2.3364262960021733, 2.6886856829951284, 2.4763251449985546, 3.2651733480015537, 2.635341139997763, 3.0092442120003398, 2.689875016003498, 2.4454356639980688, 4.60895407100179, 3.4382438870015903, 3.5837015680008335, 2.466151869004534, 4.408192898001289, 3.066398479997588, 2.924130482999317, 2.7638447680001264, 2.5502520539957914, 3.6947263129986823, 3.2421087399998214, 2.320765696997114, 4.831214552999882, 4.617021992002265, 2.207500575001177, 4.298402240005089, 2.2649938089962234, 2.3035048349993303, 2.115088165002817, 2.927317444002256, 2.212804894996225, 2.4491248600024846, 2.1222120949969394, 2.896480433999386, 2.9202714470011415, 2.3340579139985493, 2.385282154995366, 2.8244400639960077, 2.61119146800047, 2.5370966019982006, 3.6080376059981063, 4.9770983300040825, 2.495409365998057, 2.729621592006879, 3.1903509399999166, 2.358411459994386, 3.125815417006379, 4.70694298500166, 2.736367004996282, 2.8657825960035552, 2.3026864290004596, 4.176635495001392, 2.6332684380031424, 3.4680435440022848, 2.326727026993467, 2.666200731997378, 2.7874691410033847] # A Path [227, 150, 144, 196, 202, 193, 166, 226, 131, 194, 171, 192, 214, 245, 142, 154, 201, 178, 242, 187, 158, 140, 158, 153, 199, 184, 157, 150, 140, 171, 132, 154, 143, 157, 187, 208, 143, 129, 199, 171, 242, 177, 115, 170, 137, 135, 133, 159, 196, 158, 158, 146, 149, 147, 177, 197, 135, 167, 145, 134, 148, 148, 197, 173, 135, 135, 175, 154, 230, 131, 135, 131, 158, 122, 163, 128, 163, 178, 129, 133, 153, 164, 159, 165, 191, 134, 151, 170, 170, 166, 254, 152, 152, 141, 197, 161, 171, 136, 162, 162] # A Traj [374, 222, 207, 334, 327, 303, 259, 346, 175, 314, 265, 292, 351, 387, 215, 238, 322, 313, 424, 286, 256, 208, 248, 231, 311, 307, 244, 216, 217, 277, 206, 222, 201, 247, 321, 363, 234, 181, 347, 262, 407, 262, 172, 264, 204, 199, 220, 237, 306, 246, 244, 220, 213, 220, 264, 306, 204, 245, 237, 195, 224, 254, 335, 282, 206, 188, 275, 227, 357, 190, 204, 185, 255, 179, 238, 178, 248, 272, 187, 184, 235, 254, 246, 256, 303, 191, 230, 256, 281, 277, 436, 237, 238, 216, 323, 249, 280, 198, 240, 275] # A Time [0.07061816699570045, 0.03561878899927251, 0.03560868399654282, 0.08036649100540671, 0.0560449240001617, 0.04730196500167949, 0.050683164001384284, 0.047603803999663796, 0.03566566300287377, 0.08309137099422514, 0.04636236299847951, 0.040926813999249134, 0.05069077700318303, 0.055791013000998646, 0.037827076004759874, 0.05790038000122877, 0.05771599100262392, 0.09435293600108707, 0.0865061010044883, 0.050664098002016544, 0.04386282800260233, 0.062197267994633876, 0.03787423099856824, 0.0394925489963498, 0.0462508609998622, 0.11970190900319722, 0.05399023799691349, 0.04693971700180555, 0.041112494000117294, 0.051874250995751936, 0.049439210997661576, 0.03212486099801026, 0.0384016710013384, 0.04030989099555882, 0.056449920004524756, 0.06599484099569963, 0.04911540600005537, 0.024399866997555364, 0.07492404300137423, 0.03671874499559635, 0.10938456500298344, 0.038540469999134075, 0.03353274899563985, 0.04326756700174883, 0.042594409998855554, 0.032850046998646576, 0.04279405799752567, 0.036016707999806385, 0.03893460600374965, 0.04731934500159696, 0.03964214200095739, 0.04277049400116084, 0.03337579300568905, 0.050479965000704397, 0.0401813200005563, 0.06482292000146117, 0.032854410994332284, 0.05456081799638923, 0.040959198006021325, 0.0308768710019649, 0.037501573002373334, 0.044902601999638136, 0.054274786001769826, 0.057376698998268694, 0.05095900499873096, 0.025253388004784938, 0.04284013599681202, 0.038211708997550886, 0.060764082998503, 0.03160395699524088, 0.03608346200053347, 0.031007767000119202, 0.056647854995389935, 0.031723635001981165, 0.0380931289982982, 0.03402677999838488, 0.04247030400438234, 0.04365488600160461, 0.030421755000134, 0.02743830699910177, 0.05615469099575421, 0.04903912099689478, 0.04046323800139362, 0.04238558799988823, 0.04669607499818085, 0.02970003499649465, 0.04160853000212228, 0.0392998110037297, 0.05072656899574213, 0.050890824997622985, 0.0865952149979421, 0.04368172699469142, 0.049587899004109204, 0.03931668499717489, 0.05433915400499245, 0.038693433001753874, 0.06851091200223891, 0.029626386996824294, 0.043197765000513755, 0.07501617199886823] # starting iteration 5 # NN Path # NN Traj [212, 190, 188, 166, 284, 184, 148, 164, 162, 232, 178, 318, 194, 234, 230, 144, 124, 224, 254, 156, 258, 334, 166, 216, 170, 176, 168, 236, 242, 226, 322, 174, 166, 172, 602, 252, 160, 234, 176, 138, 178, 198, 252, 202, 162, 168, 156, 254, 184, 186, 312, 172, 212, 254, 234, 176, 254, 150, 300, 154, 266, 252, 206, 372, 158, 202, 234, 204, 214, 160, 202, 576, 160, 144, 406, 246, 166, 174, 204, 150, 188, 208, 312, 154, 188, 130, 244, 216, 252, 292, 308, 184, 118, 160, 140, 344, 290, 234, 224, 250] # NN Time [1.7318937059972086, 1.4335537569932058, 1.481015829005628, 1.2245296309993137, 2.0541704220013344, 1.4664190749972477, 1.1336085560033098, 1.3133260289978352, 1.3056308459999855, 1.7602681030039093, 1.3435901560005732, 2.261394819004636, 1.3946657330016023, 1.7946401799999876, 1.6341051390045322, 1.1778619249962503, 0.9469989229983184, 1.5845646759989904, 2.088121784996474, 1.2608444840006996, 1.969210896997538, 2.211122686996532, 1.3583837909973226, 1.770012218003103, 1.3484398729997338, 1.3351294419990154, 1.3852043299993966, 1.5318532149976818, 1.8388704309982131, 1.5982249250009772, 2.4146564190014033, 1.2711292489984771, 1.3859235600029933, 1.3612371350027388, 3.6241708419984207, 1.982974253995053, 1.443185812997399, 1.5735662049992243, 1.3825470020019566, 1.1467948270001216, 1.4344114399937098, 1.6381163590049255, 1.9830864439936704, 1.8438345969989314, 1.2448430719960015, 1.3227277500045602, 1.227147245001106, 1.9331816080011777, 1.550973730001715, 1.4519829899945762, 2.229411315995094, 1.4160848210012773, 1.6417998309989343, 1.9293250359987724, 1.9279892179984017, 1.360531059995992, 1.99507791200449, 1.2646567350020632, 1.9889172780021909, 1.222799565999594, 1.9458842120002373, 1.937217958002293, 1.516264065001451, 2.4854955570044694, 1.3630947579949861, 1.5332191120032803, 1.8487608170034946, 1.5912401040041004, 1.6033044950017938, 1.3078651609976077, 1.6031437489946256, 3.515783171998919, 1.2799995390014374, 1.1814900530007435, 2.7752617849982926, 2.0488686169992434, 1.277882377995411, 1.3345362229956663, 1.4688696429948322, 1.2639524319965858, 1.4412028690057923, 1.7220611199954874, 2.326739140000427, 1.229206288997375, 1.5049531369950273, 0.9448685579991434, 1.7980523830046877, 1.6246618699951796, 1.9249063589959405, 2.128387037999346, 2.0297466589981923, 1.5331655710033374, 1.0335912729933625, 1.2292772520013386, 1.082191690999025, 2.3991355129983276, 2.100800722000713, 1.7480480480007827, 1.6459673539939104, 1.8535421679989668] # CNN Path [136, 161, 158, 146, 193, 143, 232, 165, 170, 187, 154, 203, 157, 191, 154, 140, 122, 220, 233, 147, 338, 204, 163, 143, 180, 195, 145, 150, 174, 169, 214, 206, 150, 147, 295, 325, 147, 149, 153, 129, 156, 159, 172, 203, 148, 165, 169, 198, 160, 150, 148, 150, 127, 144, 180, 147, 201, 149, 188, 132, 152, 211, 143, 282, 140, 169, 176, 160, 130, 163, 161, 222, 176, 128, 248, 175, 146, 158, 168, 136, 180, 235, 224, 144, 213, 118, 177, 214, 166, 220, 197, 174, 136, 147, 124, 192, 191, 175, 172, 190] # CNN Traj [144, 176, 172, 162, 240, 168, 292, 196, 200, 212, 170, 240, 174, 218, 174, 146, 124, 270, 312, 168, 496, 278, 184, 154, 216, 222, 158, 164, 196, 208, 260, 234, 164, 170, 412, 446, 166, 164, 168, 144, 186, 176, 202, 242, 164, 200, 194, 242, 186, 174, 164, 168, 136, 164, 228, 160, 260, 166, 234, 150, 174, 230, 164, 406, 154, 194, 214, 202, 144, 186, 168, 300, 198, 134, 364, 192, 160, 188, 190, 150, 204, 278, 262, 166, 250, 124, 198, 248, 208, 274, 292, 214, 148, 156, 132, 258, 230, 212, 204, 218] # CNN Time [2.2101541810043273, 2.637010778998956, 2.5148024830050417, 2.4190002270042896, 3.396242760005407, 2.3799349370019627, 4.162605943005474, 2.822780873000738, 2.906385271002364, 3.18623689100059, 2.4163168919985765, 3.5311093279960915, 2.657739037000283, 3.367911016001017, 2.515011299001344, 2.3102042370010167, 1.7405141150011332, 3.8562724539951887, 4.378857431001961, 2.558615517002181, 6.022772251999413, 3.478601411996351, 2.697987617000763, 2.3514243499957956, 3.074113174996455, 3.3259353060057038, 2.6350858119985787, 2.426415112000541, 2.691685393001535, 2.8859519450052176, 3.805203438998433, 3.6894731450011022, 2.4064747049997095, 2.3802964120040997, 5.2067420759995, 5.970456706003461, 2.6696785789972637, 2.320921177000855, 2.379995045994292, 2.4055494209969766, 3.02250561099936, 2.721476475999225, 3.123631636000937, 3.8403694999942672, 2.6347766790058813, 2.8287294629990356, 2.923654164005711, 3.6146745799997007, 2.8577235659977305, 2.7448356649983907, 2.570392150999396, 2.5478749419999076, 2.144086560001597, 2.4167775850000908, 3.1480459779995726, 2.417114740004763, 3.7557246459982707, 2.5084239709976828, 2.9786898179954733, 2.1974716910044663, 2.602295091994165, 3.724559860995214, 2.5673090419950313, 5.213749285998347, 2.275853310005914, 2.815390806994401, 3.298990324001352, 2.987071087998629, 2.513460675996612, 2.5933449000003748, 2.5926799070002744, 4.1394474179978715, 3.119122399999469, 1.907888608999201, 5.067462979000993, 3.0553873230019235, 2.365879789002065, 2.7746517429986852, 2.816144748001534, 2.149704080999072, 3.1956593799986877, 4.206670930994733, 3.8237263439950766, 2.403771751000022, 3.8073872759996448, 1.751524725004856, 3.4297701160030556, 3.7890257309991284, 3.0357705610003904, 3.7582790619999287, 3.4080161890015006, 3.141221568999754, 2.2923136629979126, 2.4817621479960508, 1.9796879350033123, 3.436639658000786, 3.3001433519966668, 3.233613862998027, 3.1434947789966827, 3.1697211589998915] # A Path [136, 157, 158, 145, 188, 142, 123, 162, 142, 184, 153, 205, 155, 166, 155, 140, 122, 179, 222, 142, 265, 198, 163, 143, 152, 193, 145, 146, 173, 170, 249, 197, 152, 147, 274, 188, 147, 149, 141, 136, 152, 159, 158, 148, 148, 164, 169, 171, 156, 144, 148, 150, 181, 144, 318, 146, 196, 148, 184, 132, 149, 191, 144, 250, 140, 160, 173, 150, 140, 163, 153, 284, 176, 128, 222, 171, 145, 157, 168, 135, 155, 168, 207, 138, 213, 118, 175, 179, 165, 132, 186, 160, 140, 147, 124, 192, 185, 171, 173, 172] # A Traj [193, 233, 214, 220, 301, 220, 187, 248, 226, 282, 224, 328, 233, 253, 244, 193, 155, 278, 389, 226, 479, 345, 248, 210, 227, 295, 224, 204, 260, 272, 386, 293, 229, 225, 455, 299, 230, 208, 230, 189, 244, 237, 236, 231, 220, 253, 253, 264, 235, 223, 218, 228, 326, 219, 551, 213, 350, 222, 299, 200, 224, 302, 226, 435, 205, 240, 282, 248, 216, 247, 224, 587, 275, 175, 417, 254, 201, 256, 250, 204, 241, 271, 323, 208, 348, 157, 277, 304, 278, 186, 318, 244, 220, 202, 175, 317, 290, 287, 290, 252] # A Time [0.04449745299643837, 0.0499301279996871, 0.030559844999515917, 0.0354758449975634, 0.04056366799341049, 0.04552153999975417, 0.05028945500089321, 0.03354224099894054, 0.05221825400076341, 0.04771601199900033, 0.044496802998764906, 0.07260918799875071, 0.040860060005798005, 0.04043629400257487, 0.04402799600211438, 0.03447472400148399, 0.020043145996169187, 0.04546789699816145, 0.09500543400645256, 0.050246030994458124, 0.10467027599952416, 0.06986970800062409, 0.040680590995179955, 0.03835360500670504, 0.0329648469996755, 0.050207001993840095, 0.034167395999247674, 0.029536854999605566, 0.041167174000293016, 0.0729934740011231, 0.09081981800409267, 0.041368320999026764, 0.03939165799965849, 0.039837293996242806, 0.09427890699589625, 0.044115978002082556, 0.04538470499392133, 0.02494836500409292, 0.037714707003033254, 0.030131371997413225, 0.04325044799770694, 0.05431523500010371, 0.05986156799917808, 0.07682564000424463, 0.03787324999575503, 0.04072606399859069, 0.0391911490005441, 0.039919814000313636, 0.05572232099802932, 0.0423446509958012, 0.038163180004630703, 0.04028728700359352, 0.05871944899990922, 0.0477580040023895, 0.202177605999168, 0.03479230599623406, 0.06428913399577141, 0.05074831199453911, 0.044198663999850396, 0.036942740000085905, 0.03617479099921184, 0.04943173200445017, 0.0407507580021047, 0.09610760800569551, 0.03497853400040185, 0.03526463299931493, 0.06048700000246754, 0.03905233700061217, 0.0468341409941786, 0.048260819996357895, 0.03965395600243937, 0.11315015100262826, 0.04635277100169333, 0.03200141400157008, 0.06743575599830365, 0.03643822899903171, 0.027300309993734118, 0.04081176999898162, 0.04102819800027646, 0.05218553799932124, 0.05725372300366871, 0.052269101004640106, 0.06302268600120442, 0.02876451699557947, 0.05879529999947408, 0.031920091998472344, 0.04976333100057673, 0.05436643100256333, 0.05668076599977212, 0.03161362399987411, 0.053109335000044666, 0.0405068609979935, 0.047271130002627615, 0.028409696999005973, 0.027190622000489384, 0.055082999999285676, 0.04414089400233934, 0.057515104999765754, 0.058819163001317065, 0.04812885799765354] # starting iteration 6 # NN Path # NN Traj [146, 200, 216, 252, 148, 196, 176, 340, 348, 180, 148, 212, 142, 196, 210, 190, 184, 298, 144, 138, 190, 200, 134, 144, 160, 170, 270, 156, 256, 232, 170, 244, 148, 186, 236, 316, 220, 212, 276, 158, 126, 190, 152, 374, 210, 142, 184, 214, 282, 140, 176, 154, 164, 254, 196, 202, 154, 160, 242, 192, 208, 214, 176, 202, 112, 182, 206, 186, 170, 150, 276, 300, 194, 310, 226, 142, 150, 182, 294, 162, 232, 266, 226, 210, 218, 244, 172, 290, 172, 238, 396, 118, 214, 186, 234, 184, 182, 246, 176, 152] # NN Time [1.1149351420026505, 1.4811068419949152, 1.5165383339990512, 1.9384041290031746, 1.1467902410004172, 1.4381154779985081, 1.4291763910005102, 2.500877730999491, 2.224375024998153, 1.3764177769990056, 1.1850680150018889, 1.6451054219942307, 1.1388070329994662, 1.4917024899987155, 1.572938882993185, 1.4913743370052543, 1.5184605800022837, 2.157181976006541, 1.1021235680018435, 1.0911988579973695, 1.5533793559952755, 1.4720166700062691, 1.129858364998654, 1.0594572889967822, 1.3727566589950584, 1.3737552339953254, 1.9196749050024664, 1.2919927039984032, 1.9292794310022146, 1.7457893769969814, 1.4582073920028051, 1.8872339699955774, 1.1712532390010892, 1.4543080009971163, 1.7680023229986546, 2.096813962001761, 1.2957369950017892, 1.7124924290037598, 2.017736394001986, 1.191560876002768, 1.1812019669960137, 1.5108307269983925, 1.1311051459997543, 2.2904118439982994, 1.5790212790016085, 1.1655253260032623, 1.3912572900007945, 1.6705253249965608, 2.1377452599990647, 1.1760041560046375, 1.4473163100046804, 1.146689712004445, 1.231930679998186, 1.7184390539987362, 1.4184770789943286, 1.6632612530011102, 1.2980123849993106, 1.2224357969971607, 1.8653242150030565, 1.4298148329980904, 1.4636210929966182, 1.7967133519996423, 1.2393788370027323, 1.549794546001067, 0.8944337189968792, 1.3612778059978154, 1.6282998230017256, 1.3992377079994185, 1.3589007279952057, 1.2963516200034064, 1.9453079730010359, 2.2174458500012406, 1.5875909039968974, 2.1339393820016994, 1.7762733609997667, 1.179785827996966, 1.2066435149972676, 1.4527653620025376, 1.9922190199940815, 1.3705445149971638, 1.7693909290028387, 1.8863785060020746, 1.839578225997684, 1.6042604169997503, 1.7565246619997197, 1.5666319630036014, 1.3674310050046188, 2.1612405329942703, 1.3481402819961659, 1.6560824519983726, 2.72246604400425, 0.8784658040021895, 1.536717884002428, 1.5125418300012825, 1.6845413140035816, 1.341191199993773, 1.2980386229974101, 1.90287083399744, 1.2444833980043768, 1.2667808179976419] # CNN Path [135, 145, 151, 198, 143, 153, 156, 208, 207, 149, 133, 157, 177, 158, 219, 156, 158, 205, 183, 127, 181, 152, 135, 134, 134, 161, 177, 151, 191, 191, 183, 183, 141, 176, 158, 197, 159, 157, 194, 145, 179, 145, 145, 134, 192, 134, 155, 170, 231, 131, 143, 146, 138, 188, 203, 160, 138, 184, 246, 148, 155, 186, 149, 174, 111, 159, 160, 172, 149, 137, 196, 201, 146, 203, 160, 115, 160, 162, 164, 132, 177, 194, 217, 172, 192, 169, 168, 216, 168, 197, 408, 115, 208, 150, 163, 149, 141, 230, 146, 150] # CNN Traj [150, 166, 168, 238, 158, 162, 176, 262, 268, 172, 142, 174, 214, 186, 260, 172, 170, 242, 218, 130, 204, 164, 138, 142, 152, 210, 204, 170, 252, 248, 222, 216, 154, 224, 184, 276, 180, 178, 228, 164, 206, 166, 164, 158, 222, 144, 168, 190, 276, 140, 168, 164, 154, 244, 244, 190, 150, 206, 332, 158, 178, 240, 166, 196, 114, 178, 176, 198, 168, 154, 254, 234, 164, 246, 182, 122, 174, 200, 206, 152, 208, 234, 276, 188, 276, 194, 196, 272, 206, 248, 618, 118, 266, 166, 198, 158, 162, 290, 152, 194] # CNN Time [2.2531712709969725, 2.68887854499917, 2.323070287995506, 3.462233958998695, 2.3181391499965684, 2.4211861840012716, 2.6106587309986935, 4.145914416003507, 3.5182748300067033, 2.6847495820038603, 2.0701015449958504, 2.5418479569998453, 2.920845141998143, 2.872441034996882, 3.7209890160011128, 2.697945407999214, 2.7637551719963085, 3.553757328998472, 2.8042781940021086, 1.9595503689997713, 3.303078215001733, 2.5046079239982646, 2.066357688003336, 2.1182428739994066, 2.4064575009979308, 2.7978699000013876, 3.042420931997185, 2.7911358429992106, 3.724931992997881, 3.6191866750014015, 3.223847048000607, 3.2435151529934956, 2.3760645860020304, 3.1952832469978603, 2.9118310189951444, 3.25472532799904, 2.633994279000035, 2.6013931540001067, 3.647335462999763, 2.346596508003131, 3.1993014000036055, 2.7195605500019155, 2.3794298419961706, 2.388540756001021, 3.4273303610025323, 2.298682740001823, 2.8371589189991937, 3.088824086000386, 3.9742962010059273, 2.203048099996522, 2.5154155749987694, 2.4084279790040455, 2.263793631995213, 2.9475570040012826, 3.230900151997048, 2.7916285729952506, 2.3434383010026067, 3.069099611006095, 4.656929138996929, 2.4421627260016976, 2.825427881005453, 3.466584891000821, 2.501660940994043, 2.8684969460009597, 1.6743089290030184, 2.7157667970022885, 2.7180007839997415, 3.1678285020025214, 2.481655273004435, 2.3880968330049654, 3.4039784029955626, 3.6466976200026693, 2.546758240998315, 3.1436925559974043, 2.782386929000495, 1.8701692190006725, 2.512443555002392, 3.0044330739983707, 3.046715520998987, 2.258136149001075, 2.8144807559947367, 3.5057239570014644, 3.8315123499996844, 2.895725881004182, 3.5629063689993927, 2.6970567269963794, 2.825887111001066, 4.05135087799863, 3.1410043420037255, 3.4636315749958158, 7.264295638000476, 1.5959760010009632, 3.572325301996898, 2.6108212060062215, 2.958364662998065, 2.393289677995199, 2.5044039729982615, 4.2478509309949, 2.1864486839986057, 2.795851668997784] # A Path [135, 150, 132, 200, 136, 156, 156, 221, 185, 148, 133, 156, 170, 160, 192, 155, 156, 201, 179, 127, 180, 150, 144, 133, 134, 148, 176, 144, 177, 184, 183, 178, 192, 176, 158, 170, 153, 157, 199, 139, 168, 163, 143, 169, 173, 134, 154, 157, 209, 131, 148, 145, 162, 177, 189, 162, 138, 181, 203, 148, 156, 182, 147, 174, 111, 159, 160, 172, 146, 137, 191, 203, 146, 200, 144, 115, 144, 152, 163, 135, 181, 231, 200, 170, 205, 166, 146, 205, 167, 194, 248, 115, 203, 152, 165, 148, 137, 209, 146, 132] # A Traj [204, 234, 207, 325, 197, 227, 236, 366, 289, 238, 192, 227, 248, 264, 300, 230, 229, 325, 271, 175, 279, 218, 209, 185, 209, 232, 289, 213, 294, 297, 287, 281, 285, 305, 241, 263, 237, 238, 323, 199, 266, 247, 211, 271, 279, 196, 218, 232, 330, 193, 243, 217, 253, 275, 303, 264, 211, 265, 365, 213, 254, 303, 211, 259, 149, 237, 254, 272, 221, 220, 318, 328, 222, 326, 213, 168, 202, 248, 279, 212, 287, 387, 313, 246, 346, 245, 221, 334, 268, 311, 410, 141, 327, 238, 285, 204, 215, 338, 199, 201] # A Time [0.03786751100415131, 0.040371073002461344, 0.041894098998454865, 0.04628341400530189, 0.03198538099968573, 0.04007523700420279, 0.03669220300071174, 0.07410292299755383, 0.04419955399498576, 0.03870122200169135, 0.03971343799639726, 0.03059276299609337, 0.0358567570001469, 0.05306914900575066, 0.05162957299762638, 0.04027401700295741, 0.03086629199970048, 0.04734099699999206, 0.04127791400242131, 0.04525191100401571, 0.05912800000078278, 0.0505961090020719, 0.0338247620020411, 0.06594135200430173, 0.03618428900517756, 0.05202605199883692, 0.041186697999364696, 0.03036705699923914, 0.05603425499430159, 0.0479708419952658, 0.058814239004277624, 0.04932733099849429, 0.057926223998947535, 0.06848752299993066, 0.039820705002057366, 0.05127776000153972, 0.0448667189994012, 0.034822952999093104, 0.048342895999667235, 0.03260853599931579, 0.04578517800109694, 0.041522814994095825, 0.03101451299880864, 0.05137504999584053, 0.043555987002037, 0.04053794799983734, 0.05392430300707929, 0.04455017599684652, 0.05910811400099192, 0.03786500400019577, 0.05768575399997644, 0.03777574400010053, 0.040561504996730946, 0.057067731002462097, 0.050537934999738354, 0.05308939699898474, 0.044160234996525105, 0.04804736499499995, 0.07938856900000246, 0.03392071399866836, 0.048853686996153556, 0.06342391399812186, 0.029776835996017326, 0.034815309998521116, 0.027736692005419172, 0.03250991400273051, 0.04637848899437813, 0.04007335999631323, 0.04138940799748525, 0.04257640500145499, 0.06167567399825202, 0.05324266399838962, 0.035929852005210705, 0.09784056500211591, 0.04952715399849694, 0.03209116899961373, 0.0367989270016551, 0.036450633000640664, 0.05603955099650193, 0.0447045659966534, 0.056116098996426445, 0.07133448300010059, 0.06492134099971736, 0.043981292998068966, 0.06168718499975512, 0.03994407699792646, 0.04217830800189404, 0.06577156799903605, 0.03999322800518712, 0.08106746400153497, 0.08633195599395549, 0.023085134002030827, 0.0685209430012037, 0.03749219999735942, 0.048772641996038146, 0.03270064500247827, 0.03453439699660521, 0.06070374599948991, 0.029329254997719545, 0.0620462349979789] # starting iteration 7 # NN Path # NN Traj [212, 464, 170, 278, 146, 202, 148, 146, 234, 230, 204, 196, 170, 178, 212, 234, 110, 248, 142, 184, 278, 236, 168, 188, 380, 324, 244, 168, 332, 156, 160, 254, 144, 160, 210, 178, 220, 152, 160, 192, 148, 172, 314, 206, 190, 194, 162, 146, 168, 154, 170, 148, 194, 262, 132, 266, 160, 208, 148, 142, 184, 234, 258, 204, 208, 252, 136, 152, 190, 180, 218, 190, 320, 206, 218, 192, 134, 148, 162, 232, 136, 136, 176, 182, 180, 304, 190, 198, 192, 158, 178, 136, 170, 172, 186, 138, 242, 236, 160, 232] # NN Time [1.5754261949987267, 3.336326790995372, 1.2986436310020508, 1.9172266490058973, 1.1771187009944697, 1.5470458929994493, 1.262703751999652, 1.1490406919983798, 1.7547465309980907, 1.6314402149946545, 1.5534527290001279, 1.3502708060041186, 1.374598877999233, 1.3217835309988004, 1.6978266639998765, 1.747476328004268, 0.8965261089979322, 1.7800073300022632, 1.135191499000939, 1.4998046699984116, 1.8583269370064954, 1.7729348189968732, 1.3651930499981972, 1.4208261499952641, 2.6178016330013634, 2.380779626000731, 1.6476998149955762, 1.3194611029975931, 2.3312305829967954, 1.1563692679992528, 1.2486832350041368, 1.8846347149956273, 1.1256364060027408, 1.2282854430013685, 1.481729589002498, 1.3592747489965404, 1.5788376969940146, 1.2732560570002533, 1.1638433190018986, 1.4393587049999041, 1.1607202900049742, 1.379754128996865, 1.997019784997974, 1.5277147810047609, 1.4411157300055493, 1.3835800729939365, 1.1348314839997329, 1.1626543829988805, 1.2608721599972341, 1.2244211949946475, 1.3367154770021443, 1.2879862420013524, 1.5422616239957279, 2.000302214997646, 1.0211651450008503, 1.9803685909937485, 1.3385964589979267, 1.5606801620015176, 1.2508726400046726, 1.0007301090008696, 1.2450511169954552, 1.9419121989994892, 1.7693614539966802, 1.5117936700044083, 1.5733792989994981, 2.064589817004162, 1.06404575099441, 1.2382377999965684, 1.448909195001761, 1.3585062109996215, 1.638282485997479, 1.6085974689995055, 2.3296008839970455, 1.6760424120002426, 1.6011429450009018, 1.6466537109954515, 1.0999795950046973, 1.095948448004492, 1.3639748810019228, 1.8372712100026547, 1.0504212580053718, 1.1474866160060628, 1.4008254630025476, 1.4010239599956549, 1.4244074410016765, 2.1907156230008695, 1.6808610760053853, 1.4584251729975222, 1.4625113130023237, 1.124634095998772, 1.3310427169999457, 1.0430530250014272, 1.284545968999737, 1.3196609019942116, 1.5488269589986885, 1.0346394520020112, 1.6147548129956704, 1.623109811996983, 1.2488083909993293, 1.728096805003588] # CNN Path [177, 342, 164, 191, 125, 149, 133, 174, 159, 189, 158, 158, 155, 134, 275, 183, 108, 179, 137, 162, 182, 172, 181, 154, 273, 203, 177, 197, 149, 146, 135, 179, 133, 166, 142, 142, 151, 149, 137, 140, 130, 148, 203, 150, 229, 146, 148, 155, 141, 139, 261, 150, 164, 200, 130, 255, 142, 180, 136, 130, 132, 195, 172, 171, 149, 412, 131, 167, 164, 156, 194, 160, 205, 171, 189, 155, 124, 132, 143, 163, 125, 135, 149, 163, 176, 253, 175, 167, 145, 149, 123, 124, 139, 167, 170, 119, 143, 163, 145, 197] # CNN Traj [210, 424, 190, 244, 148, 170, 146, 206, 200, 244, 178, 204, 162, 146, 324, 222, 110, 208, 154, 182, 230, 226, 214, 182, 356, 252, 204, 230, 166, 156, 156, 228, 134, 194, 150, 162, 176, 172, 164, 150, 150, 166, 288, 180, 274, 172, 174, 168, 160, 156, 344, 168, 182, 236, 132, 306, 160, 202, 144, 136, 136, 222, 202, 196, 166, 570, 134, 200, 196, 180, 244, 180, 254, 186, 250, 186, 130, 136, 158, 188, 132, 140, 168, 170, 210, 296, 184, 194, 156, 164, 136, 128, 154, 174, 186, 126, 166, 192, 154, 244] # CNN Time [2.8551359349949053, 6.064577789002215, 2.872663202993863, 3.1324545230017975, 2.034793136997905, 2.577578871998412, 2.2976538529983372, 2.888164873998903, 2.74046450300375, 3.4004971309987013, 2.581954572997347, 2.5778944840058102, 2.8037395360006485, 2.259713516003103, 4.641530842993234, 3.154780775003019, 1.5345199570001569, 3.2510183039994445, 2.4233820069930516, 2.8041783119988395, 2.9199947390006855, 2.93169881200447, 3.3840087040007347, 2.768172695999965, 5.009917176001181, 3.76318300699495, 3.0456492899975274, 3.2612001599991345, 2.6651505249974434, 2.383811285995762, 2.2301275040008477, 3.2794513530025142, 1.861158225001418, 2.558983574999729, 2.3571275970025454, 2.3616339080035686, 2.7032535030011786, 2.81773910800257, 2.1697992449990124, 2.3476024880001205, 2.231797918997472, 2.5013874169962946, 4.013388671002758, 2.61229881500185, 4.087144181001349, 2.6875054289994296, 2.7116194530026405, 2.4924810390002676, 2.0413856799932546, 2.3161762219970115, 4.759205219997966, 2.51064109300205, 3.0007747909985483, 3.576725113001885, 1.8941297729979851, 4.394356471995707, 2.4631747719977284, 2.8950516340046306, 2.1752127229992766, 2.149483399996825, 2.071520875004353, 3.2606715129950317, 2.7977014019998023, 2.7967861580036697, 2.6598376980036846, 7.082856476998131, 2.1368903850016068, 2.831740637004259, 2.7880054819979705, 2.4785531890011043, 3.2983912820054684, 2.9841862190005486, 3.689757894993818, 2.7712325880056596, 3.236111551996146, 2.7215346270022565, 2.1097945860019536, 1.9951008619973436, 2.3214141719945474, 2.875163112003065, 2.179631792998407, 2.1776381800009403, 2.46772718399734, 2.671910762997868, 2.938783274999878, 4.05967489300383, 2.997166898996511, 2.9256380980004906, 2.3999262810029904, 2.209495645998686, 2.114637781000056, 1.926434042994515, 2.2410515939991456, 2.575210650000372, 2.9213277020026, 1.865340094002022, 2.393091714999173, 2.815965974004939, 2.268281291006133, 3.331038060001447] # A Path [177, 322, 156, 189, 123, 156, 138, 134, 171, 183, 158, 157, 155, 134, 185, 175, 108, 172, 143, 165, 181, 167, 143, 154, 268, 203, 175, 187, 149, 146, 140, 181, 133, 166, 142, 140, 159, 149, 137, 140, 134, 146, 206, 150, 192, 145, 148, 135, 141, 139, 247, 149, 165, 200, 130, 231, 140, 163, 137, 130, 132, 180, 170, 164, 147, 243, 131, 139, 156, 179, 194, 160, 204, 168, 168, 146, 124, 132, 140, 158, 125, 135, 148, 163, 154, 219, 175, 160, 160, 146, 123, 124, 139, 167, 170, 119, 160, 156, 142, 174] # A Traj [270, 515, 248, 311, 180, 248, 213, 196, 255, 326, 235, 256, 220, 200, 292, 284, 137, 270, 220, 255, 284, 275, 232, 246, 465, 347, 269, 276, 232, 198, 220, 336, 165, 246, 207, 208, 240, 249, 214, 196, 217, 213, 377, 239, 313, 229, 233, 206, 199, 207, 414, 224, 259, 314, 169, 365, 216, 243, 201, 176, 168, 269, 257, 241, 228, 395, 172, 216, 229, 288, 327, 251, 343, 242, 261, 229, 182, 176, 210, 253, 177, 192, 224, 232, 242, 346, 254, 248, 234, 202, 190, 166, 206, 230, 256, 158, 270, 232, 204, 269] # A Time [0.050531716995465104, 0.23455288200057112, 0.03977635900082532, 0.07589692100009415, 0.036470442995778285, 0.039032915999996476, 0.03729295900120633, 0.029906997995567508, 0.04388091999862809, 0.06471825499465922, 0.04032361899589887, 0.05539827099710237, 0.035116418002871796, 0.03135401000326965, 0.05619480799941812, 0.04674162199808052, 0.022096959997725207, 0.045198447005532216, 0.06251419299951522, 0.043601479002973065, 0.050777611999365035, 0.055914060001668986, 0.04764662699744804, 0.04367006099346327, 0.12137611000071047, 0.07626634200278204, 0.03947034700104268, 0.04422362699551741, 0.0471987370037823, 0.034515993000240996, 0.046360355998331215, 0.0651923559998977, 0.017581664003955666, 0.03539825600455515, 0.043514811994100455, 0.028869344998383895, 0.04970002699701581, 0.04443005400389666, 0.03909466000186512, 0.027286999000352807, 0.04221708600380225, 0.03599352800665656, 0.10266916000546189, 0.03962358099670382, 0.06201037999562686, 0.037677610001992434, 0.055570434997207485, 0.043382839001424145, 0.028179722001368646, 0.0352031389993499, 0.07826448600098956, 0.04167796699766768, 0.06175006100238534, 0.04671906099974876, 0.027843258998473175, 0.07001252399641089, 0.04542425299587194, 0.04444617699482478, 0.03096443400136195, 0.027229748004174326, 0.022624878001806792, 0.03680260399414692, 0.03983225399861112, 0.041831567003100645, 0.039433779995306395, 0.08490565900137881, 0.026728138996986672, 0.051449044003675226, 0.034147389997087885, 0.058189980998577084, 0.0569877070010989, 0.051290770003106445, 0.06625866000103997, 0.04196164799941471, 0.06162099899665918, 0.04235526199772721, 0.034618507997947745, 0.023962669001775794, 0.03633897200052161, 0.03832576499553397, 0.028912580004543997, 0.030854280994390137, 0.03547737099870574, 0.03618219800409861, 0.03579009399982169, 0.058342871998320334, 0.04423345900431741, 0.038824843999464065, 0.032981878001010045, 0.023222429997986183, 0.03900691899616504, 0.026825471002666745, 0.03159118499752367, 0.036084988998482004, 0.04439543299668003, 0.025249803999031428, 0.07893754999531666, 0.04941809799493058, 0.032239012005447876, 0.049319025994918775] # starting iteration 8 # NN Traj [172, 254, 266, 322, 172, 224, 282, 140, 202, 242, 354, 166, 294, 216, 156, 182, 142, 200, 178, 174, 214, 214, 154, 178, 220, 154, 382, 202, 146, 192, 228, 230, 176, 188, 196, 220, 158, 216, 288, 162, 180, 282, 260, 120, 230, 248, 168, 152, 224, 202, 140, 182, 132, 252, 262, 252, 168, 174, 238, 128, 216, 166, 154, 232, 184, 240, 240, 190, 168, 234, 224, 224, 144, 120, 130, 202, 224, 134, 194, 202, 172, 230, 142, 176, 162, 330, 190, 194, 234, 192, 154, 156, 158, 198, 206, 168, 176, 158, 166, 222] # NN Time [1.2684204199977103, 1.8228114590019686, 1.5331745039948146, 2.3316027199980454, 1.3455352519958979, 1.6307125449966406, 2.2331789450036013, 1.1197107429979951, 1.4146612140029902, 2.002509171004931, 2.4400168040010612, 1.252838675995008, 2.105098956999427, 1.6640191420010524, 1.3469121119996998, 1.4517710789950797, 1.1547975200010114, 1.5181056450019241, 1.3986370980055653, 1.247902969000279, 1.7834685240013641, 1.7724245599965798, 1.184269358003803, 1.3679420749976998, 1.7474894680053694, 1.216215081003611, 2.5212978390045464, 1.521372285002144, 1.130177585000638, 1.5787158399980399, 1.7434368159956648, 1.971135735999269, 1.477967731996614, 1.4121514660000685, 1.5490313050031546, 1.7353673290053848, 1.26297253100347, 1.6154227829974843, 2.1172996000022977, 1.2577777989936294, 1.4135609849981847, 2.0910920030000852, 1.9000767950055888, 0.8760685019951779, 1.850052247995336, 1.8058516539967968, 1.2899275890013087, 1.2370926229996257, 1.6833438760004356, 1.5808868130043265, 1.1945615810036543, 1.4352423860036652, 1.155885929998476, 2.035421086999122, 2.1274853789946064, 1.8163081079983385, 1.2929659059955156, 1.4133630900032585, 1.8945118489937158, 1.1016036119981436, 1.5874790320012835, 1.3362761249954929, 1.1966663030034397, 1.576974584000709, 1.481250042001193, 2.049047978995077, 1.715135852995445, 1.4049845879999339, 1.3046532940061297, 1.8441160200018203, 1.7206598409975413, 1.6537260629993398, 1.201082886000222, 0.9574259549990529, 1.1489067939983215, 1.4501126239993027, 1.7292896229992039, 0.9086505439991015, 1.5726451640002779, 1.5577079430004233, 1.3258341590044438, 1.7318455549975624, 1.179254218004644, 1.441342182995868, 1.1950698860018747, 2.2136275400043814, 1.5891690089993062, 1.5451006090006558, 1.8456328259999282, 1.4044872679951368, 1.3034479979978641, 1.116914503996668, 1.0063585300013074, 1.5087586180015933, 1.7098756300038076, 1.4622240249955212, 1.3154739849996986, 1.2220294680009829, 1.3442675949991099, 1.871124516997952] # CNN Path [166, 197, 190, 135, 146, 181, 221, 130, 154, 238, 268, 143, 175, 187, 141, 157, 126, 213, 197, 175, 155, 172, 147, 144, 183, 133, 233, 181, 137, 188, 157, 167, 152, 149, 162, 213, 160, 148, 198, 162, 170, 199, 185, 121, 179, 194, 131, 142, 190, 194, 137, 149, 152, 176, 272, 176, 276, 144, 191, 152, 176, 159, 123, 169, 165, 167, 188, 143, 156, 236, 157, 174, 167, 120, 123, 152, 182, 137, 188, 173, 143, 155, 159, 156, 144, 220, 142, 150, 228, 152, 198, 131, 128, 170, 204, 157, 143, 139, 152, 182] # CNN Traj [178, 244, 224, 150, 164, 204, 272, 138, 182, 292, 412, 156, 210, 212, 154, 176, 142, 272, 244, 208, 184, 198, 166, 152, 202, 144, 306, 212, 156, 220, 176, 186, 178, 154, 188, 302, 194, 182, 250, 178, 204, 242, 210, 122, 198, 222, 136, 162, 236, 256, 142, 180, 164, 198, 328, 216, 360, 164, 232, 180, 214, 188, 126, 184, 176, 194, 208, 170, 174, 308, 180, 190, 192, 128, 130, 178, 210, 148, 222, 200, 154, 182, 178, 182, 158, 290, 160, 174, 288, 166, 226, 138, 130, 206, 252, 168, 160, 148, 172, 212] # CNN Time [2.6704374269975233, 3.505839064004249, 2.911259936998249, 2.180455597997934, 2.409049000001687, 3.0501074549974874, 3.911407332001545, 2.140579514998535, 2.538729357002012, 3.9980211109941592, 5.172530140996969, 2.43108099699748, 3.491170195004088, 3.3437694359963643, 3.0644980069992016, 2.421026341995457, 2.085454041000048, 4.194453367999813, 3.369813042001624, 2.8376835280068917, 2.4433241939987056, 3.1427288909981144, 2.3838865059951786, 2.4056971300014993, 3.2235172240034444, 2.1680799390014727, 4.375665706000291, 3.24519988099928, 2.1532423489989014, 3.376121566994698, 3.0144240820009145, 2.9419734260009136, 2.7443936109993956, 2.97678118100157, 3.026735430001281, 3.964888560003601, 2.863421592002851, 2.748570956995536, 3.562310161003552, 2.796323886999744, 2.7517923710038303, 3.767676917996141, 2.9895964870011085, 1.675749863999954, 3.0768044409996946, 3.2364617380007985, 2.0801553329947637, 2.618228719999024, 3.415536095002608, 3.650174030000926, 2.1564223910027067, 2.653540080995299, 2.3848935209971387, 2.779957074999402, 4.877344479005842, 3.144529720004357, 4.59780621399841, 2.395530827001494, 3.166151600999001, 2.6472506069985684, 3.054132056000526, 2.7645992420002585, 1.8952519070007838, 2.738783444001456, 2.6621919449971756, 3.2637458780009183, 2.9877347020010347, 2.4631866339987027, 2.5562974080021377, 4.328842538998288, 2.641174113996385, 2.769536836000043, 2.7568086859973846, 1.8502386420004768, 2.008952367999882, 2.4433885460020974, 3.1745433039977797, 2.0117158380016917, 3.197861909000494, 2.775255474000005, 2.166439321998041, 2.680792138999095, 2.4367987169971457, 2.778845670996816, 2.000644123996608, 3.9917754990019603, 2.336132201999135, 2.532640290002746, 4.181219568003144, 2.542870495002717, 3.327081368996005, 2.0558502189960564, 1.8063579019944882, 3.120889999998326, 3.6197153700049967, 2.6810765159971197, 2.3571304270008113, 2.5641484730003867, 2.6175722379994113, 3.060543380997842] # A Path [136, 139, 196, 137, 145, 194, 217, 130, 148, 185, 263, 143, 157, 187, 141, 155, 119, 206, 159, 137, 151, 166, 153, 144, 182, 133, 208, 172, 132, 185, 138, 164, 152, 149, 159, 149, 134, 148, 177, 167, 153, 221, 189, 121, 179, 192, 130, 142, 186, 191, 133, 147, 152, 172, 226, 189, 138, 167, 185, 151, 176, 157, 123, 168, 165, 150, 183, 143, 144, 196, 161, 172, 167, 117, 123, 152, 178, 136, 184, 179, 143, 154, 152, 152, 144, 205, 135, 162, 209, 151, 205, 131, 128, 157, 274, 154, 143, 137, 145, 179] # A Traj [193, 221, 298, 213, 226, 322, 358, 188, 220, 285, 507, 214, 261, 289, 202, 224, 170, 345, 246, 211, 225, 259, 229, 208, 290, 197, 371, 270, 185, 286, 207, 249, 250, 215, 255, 233, 197, 231, 272, 233, 224, 398, 281, 151, 276, 291, 182, 221, 290, 332, 183, 243, 218, 244, 369, 332, 205, 250, 312, 237, 283, 249, 164, 242, 241, 240, 268, 226, 208, 328, 243, 240, 250, 161, 172, 231, 286, 185, 300, 281, 199, 241, 218, 236, 200, 364, 202, 257, 337, 226, 312, 182, 161, 257, 444, 224, 220, 199, 222, 282] # A Time [0.027050021999457385, 0.037320210998586845, 0.04006789000413846, 0.05457702500279993, 0.03917543699935777, 0.05667156299750786, 0.06858573600038653, 0.03330866699980106, 0.03735478599992348, 0.04536983300204156, 0.09973413599800551, 0.037971752004523296, 0.04541141500521917, 0.04615031999856001, 0.038665587999275886, 0.029722539999056607, 0.055161980002594646, 0.10250408999854699, 0.04878109900164418, 0.03338833300222177, 0.033451626004534774, 0.05685451199678937, 0.040931408999313135, 0.035970363998785615, 0.054668907992891036, 0.034738030000880826, 0.07270171900017885, 0.05825601900141919, 0.027550251994398423, 0.09002513399900636, 0.038188949998584576, 0.04506101599690737, 0.04706571000133408, 0.03873533200385282, 0.0419156429998111, 0.051717695001570974, 0.039843225000367966, 0.03872554599365685, 0.04734459600149421, 0.029870957994717173, 0.05080988300323952, 0.07234241100377403, 0.03902049600583268, 0.03639800800010562, 0.04064132200437598, 0.0458469170043827, 0.03760941300424747, 0.04801510800461983, 0.042511943996942136, 0.04919732800044585, 0.030854970995278563, 0.04782653299480444, 0.030662123004731257, 0.044858114000817295, 0.05846513400319964, 0.06314379000104964, 0.028965353994863108, 0.06476067900075577, 0.06963493300281698, 0.03742511099699186, 0.0520925390010234, 0.043396465000114404, 0.027106553003250156, 0.03631799499999033, 0.03585076400486287, 0.03836904200579738, 0.03919798199785873, 0.038534988001629245, 0.031646209994505625, 0.052061110000067856, 0.04425075100152753, 0.03308086000470212, 0.042430501998751424, 0.03093948400055524, 0.028182520996779203, 0.04519180500210496, 0.03967958599969279, 0.02630404099909356, 0.050833007000619546, 0.04738311000255635, 0.02795725299802143, 0.05465244600054575, 0.03612060900195502, 0.04636515399761265, 0.026405684002384078, 0.06526302400015993, 0.04315802700148197, 0.0390529959986452, 0.05565513300098246, 0.03525301700574346, 0.06316653699468588, 0.03154392199940048, 0.0208376730006421, 0.04717710699333111, 0.07901727299758932, 0.036467961996095255, 0.03840744400076801, 0.03064909800013993, 0.04653409200545866, 0.04447939099918585] # starting iteration 9

from random import choice from timeit import default_timer as timer import tensorflow as tf import numpy as np MAZE_DIM = 50 # ANALYSIS def run_nn_agent(complete_grid, model): original_source = (0,0) # we need to remember the original_source node bc in repeated A* source changes source = original_source n, m = (len(complete_grid), len(complete_grid[0])) goal = (n-1, m-1) # dimensions of grid num_actions = trajectory = 0 complete_grid = np.array(complete_grid) grid = np.array([[0.5]*(m) for _ in range(n)]) grid[source[0]][source[1]] = 0.0 path = [source] grid_states = [] movement_grid = [[-1]*m for _ in range(n)] while source != goal: # execution step valid_actions = [] # Generate input data for agent - grid, source + neighbors tuple source_neighbors_data = [source[0], source[1]] for dx, dy in [(0, 1), (-1, 0), (0, -1), (1, 0)]: # generate children new_x, new_y = (source[0] + dx, source[1] + dy) if 0 <= new_x < len(grid) and 0 <= new_y < len(grid): source_neighbors_data.extend([new_x, new_y, grid[new_x][new_y]]) if grid[new_x][new_y] != 1: valid_actions.append((dx, dy)) else: source_neighbors_data.extend([0,0,1]) input_data = (np.array([grid]), np.array([source_neighbors_data])) # Give agent grid and (try to) take step by agent out = model(input_data).numpy() prediction = np.argmax( out , axis = 1 )[0] # Move to predicted state, if possible num_actions += 1 action_movement_dict = { 0 : (-1,0), # North 1 : (0, 1), # East 2 : (1, 0), # South 3 : (0, -1) # West } # If attempted action is not valid for some reason, pick a random one that is # Also, if we're attempting to go the same way we already have (in a loop), run a_star to get out of the loop action = action_movement_dict[prediction] if action not in valid_actions: action = choice(valid_actions) elif movement_grid[source[0]][source[1]] == prediction: valid_actions.remove(action) a_star_path, *_ = a_star(deepcopy(grid), source, goal) idx = 1 next = a_star_path[0] # Follow a_star until we hit a block or we are no longer in the loop while source in path: if idx >= len(a_star_path): # print("Finished here") break curr = a_star_path[idx] x, y = curr grid[x][y] = complete_grid[x][y] if grid[x][y] == 1: # If we hit a new block, break break # Move from source to curr trajectory += 1 source = (x, y) path.append(source) idx += 1 continue movement_grid[source[0]][source[1]] = prediction # Move to new cell x, y = (source[0] + action[0], source[1] + action[1]) grid[x][y] = complete_grid[x][y] if grid[x][y] == 1: continue trajectory += 1 source = (x, y) path.append(source) # print(f"current path: {path[-10:]}") return path, trajectory, num_actions NUM_ITERATIONS = 30 NUM_TRIALS = 100 MAZE_DIM = 50 DENSITY = 0.3 SOLVABILITY = True IS_TERRAIN_ENABLED = False source = (0,0) target = goal = (MAZE_DIM-1, MAZE_DIM-1) p1_dense_model = tf.keras.models.load_model('./test_data_dense_p1/') p1_cnn_model = tf.keras.models.load_model('./test_data_cnn_p1/') def run_iteration(i=0): # nn_path_lens = [] # nn_trajs = [] # nn_times = [] # cnn_path_lens = [] # cnn_trajs = [] # cnn_times = [] # a_path_lens = [] # a_trajs = [] # a_times = [] nn_path_sum = nn_traj_sum = nn_time_sum = 0 cnn_path_sum = cnn_traj_sum = cnn_time_sum = 0 a_path_sum = a_traj_sum = a_time_sum = 0 print(f"starting iteration {i}") for j in range(NUM_TRIALS): # print(f"trial {(i, j)}") grid, _, _ = gen_gridworld(MAZE_DIM, DENSITY, SOLVABILITY, IS_TERRAIN_ENABLED, source, target) start = timer() path, traj, actions = run_nn_agent(grid, p1_dense_model) stop = timer() nn_time = stop - start # nn_path_lens.append(len(set(path))) # nn_trajs.append(traj) # nn_times.append(nn_time) nn_path_sum += len(set(path)) nn_traj_sum += traj nn_time_sum += nn_time start = timer() path, traj, actions = run_nn_agent(grid, p1_cnn_model) stop = timer() cnn_time = stop - start # cnn_path_lens.append(len(set(path))) # cnn_trajs.append(traj) # cnn_times.append(cnn_time) cnn_path_sum += len(set(path)) cnn_traj_sum += traj cnn_time_sum += cnn_time start = timer() _, repeated_a_star_path_set, _, _, _, _, traj, _, _ = repeated_a_star(grid) stop = timer() a_star_time = stop - start # a_path_lens.append(len(repeated_a_star_path_set)) # a_trajs.append(traj) # a_times.append(a_star_time) a_path_sum += len(repeated_a_star_path_set) a_traj_sum += traj a_time_sum += a_star_time return ((nn_path_sum / NUM_TRIALS, nn_traj_sum / NUM_TRIALS, nn_time_sum / NUM_TRIALS), (cnn_path_sum / NUM_TRIALS, cnn_traj_sum / NUM_TRIALS, cnn_time_sum / NUM_TRIALS), (a_path_sum / NUM_TRIALS, a_traj_sum / NUM_TRIALS, a_time_sum / NUM_TRIALS)) # print("NN Path", nn_path_lens) # print("NN Traj", nn_trajs) # print("NN Time", nn_times) # print("CNN Path", cnn_path_lens) # print("CNN Traj", cnn_trajs) # print("CNN Time", cnn_times) # print("A Path", a_path_lens) # print("A Traj", a_trajs) # print("A Time", a_times) nn_path_avgs = [] nn_traj_avgs = [] nn_time_avgs = [] cnn_path_avgs = [] cnn_traj_avgs = [] cnn_time_avgs = [] a_path_avgs = [] a_traj_avgs = [] a_time_avgs = [] for i in range(NUM_ITERATIONS): rets = run_iteration(i) nn_path_avgs.append(rets[0][0]) nn_traj_avgs.append(rets[0][1]) nn_time_avgs.append(rets[0][2]) cnn_path_avgs.append(rets[1][0]) cnn_traj_avgs.append(rets[1][1]) cnn_time_avgs.append(rets[1][2]) a_path_avgs.append(rets[2][0]) a_traj_avgs.append(rets[2][1]) a_time_avgs.append(rets[2][2]) print(nn_path_avgs) print(nn_traj_avgs) print(nn_time_avgs) print(cnn_path_avgs) print(cnn_traj_avgs) print(cnn_time_avgs) print(a_path_avgs) print(a_traj_avgs) print(a_time_avgs)

nn_path_avgs = [177.4, 157.6, 175.6, 156.2, 189.4, 188.0, 162.4, 164.8, 151.8, 184.8] cnn_path_avgs = [226.0, 176.2, 157.0, 142.6, 158.8, 175.4, 172.0, 161.0, 158.2, 215.4] a_path_avgs = [130.2, 132.2, 127.4, 123.0, 123.4, 133.4, 125.8, 127.0, 120.6, 139.8] nn_traj_avgs = [222.0, 180.8, 217.6, 184.8, 251.6, 245.6, 205.6, 192.4, 176.8, 226.0] cnn_traj_avgs = [298.4, 206.8, 180.4, 157.2, 184.8, 207.2, 205.2, 184.8, 188.8, 267.2] a_traj_avgs = [258.2, 251.2, 230.6, 206.4, 250.0, 263.0, 269.6, 234.0, 231.2, 287.4] nn_time_avgs = [1.934705733403098, 1.645674962400517, 1.8060789418013883, 1.6095205746009014, 2.0631797833993915, 2.0514500179997412, 1.8833281233994057, 1.617990298998484, 1.6302852630004054, 2.040595890399709] cnn_time_avgs = [4.266449646399996, 3.369963846601604, 2.9987810671998885, 2.577833686601662, 3.0480733748001514, 3.3660197225995945, 3.5337548686002265, 3.025409155401576, 3.049412638200738, 4.3841205936012555] a_time_avgs = [0.04768102299858583, 0.04122461159859085, 0.03712173440144397, 0.03736739440064411, 0.04637555099907331, 0.0517265602000407, 0.0513785564005957, 0.034284745600598396, 0.0453821864008205, 0.056263956600741946]

import matplotlib.pyplot as plt its = [0,1,2,3,4,5,6,7,8,9] plt.plot(its, nn_path_avgs) plt.plot(its, cnn_path_avgs) plt.plot(its, a_path_avgs) plt.legend(["NN", "CNN", "A Star"]) plt.xlabel("Iterations") plt.ylabel("Average Path") plt.title("Average Path For Each Agent") plt.grid() plt.show()

its = [0,1,2,3,4,5,6,7,8,9] plt.plot(its, nn_traj_avgs) plt.plot(its, cnn_traj_avgs) plt.plot(its, a_traj_avgs) plt.legend(["NN", "CNN", "A Star"]) plt.xlabel("Iterations") plt.ylabel("Average Trajectory") plt.title("Average Trajectory For Each Agent") plt.grid() plt.show()

its = [0,1,2,3,4,5,6,7,8,9] plt.plot(its, nn_time_avgs) plt.plot(its, cnn_time_avgs) plt.plot(its, a_time_avgs) plt.legend(["NN", "CNN", "A Star"]) plt.xlabel("Iterations") plt.ylabel("Average Times") plt.title("Average Times For Each Agent") plt.grid() plt.show()

class CellState(Enum): BLOCKED = 1 OPEN = 2 UNKNOWN = 3 def __int__(self): return self.value class Intel: def __init__(self, N: int = 0): self.N = N self.visited = False self.status = CellState.UNKNOWN self.C = -1 # Value of -1 means we don't know this value (cell hasn't been visited yet) self.B = 0 self.E = 0 self.H = N # self.blocked_probability = 0.5 # Used for agent 5 def __str__(self): return f"N: {self.N}, visited: {self.visited}, status: {self.status}, C: {self.C}, B: {self.B}, " \ f"E: {self.E}, H: {self.H}" def __repr__(self): return self.__str__() @staticmethod def convert(self): return np.array([self.N, int(self.visited), int(self.status), self.C, self.B, self.E, self.H]) # didn't add blocked_probability bc we only care about agent 3 INTEL_SIZE = len(Intel().__dict__.keys()) def _run_single_inference_agent3(grid_len: int, kb: List[List[Intel]]) -> bool: changed = False for row_idx, row in enumerate(kb): for col_idx, intel in enumerate(row): # If the cell has not been visited yet, we can't infer anything about its neighbors # since C has not been set # If Hx=0: nothing remains to be inferred about cell x. if not intel.visited or intel.H == 0: continue # If Cx=Bx: all remaining hidden neighbors of x are empty. if intel.C == intel.B: for (n_row, n_col) in _get_neighbor_indices(grid_len, row_idx, col_idx): if kb[n_row][n_col].status == CellState.UNKNOWN: update_cell_status(CellState.OPEN, kb, n_row, n_col, grid_len) changed = True # If Nx−Cx=Ex: all remaining hidden neighbors of x are blocked. elif intel.N - intel.C == intel.E: for (n_row, n_col) in _get_neighbor_indices(grid_len, row_idx, col_idx): if kb[n_row][n_col].status == CellState.UNKNOWN: update_cell_status(CellState.BLOCKED, kb, n_row, n_col, grid_len) changed = True return changed def agent3(grid: List[List[int]], dataformat:int = None, inference: Callable = _run_single_inference_agent3): """ Args: Returns: list(tuple(int, int)): the path from the source node to the goal node which consists of coordinate points (i.e. x,y) in the form of tuples. int: the length of the discovered path from source to goal (i.e. the length of the first return value) list(tuple(int,int)): all visited squares from the source point. Contains int tuples in the form of (x,y). int: trajectory - num of moves the agent makes int: num of times the agent assumes wrong and enters a blocked square """ knowledge_base = create_initial_kb(grid) grid_len = len(grid) n, m = grid_len-1, grid_len-1 source, goal = (0, 0), (n,m) full_path = [source] full_path_and_blocks = [] solvable = True num_bumps = 0 # seen = defaultdict(int) grid_states = [] grid_sources = [] actions = [] while solvable and goal != full_path[-1]: # Create current grid based on current knowledge base current_grid = np.zeros((len(grid), len(grid[0]))) for row_idx, row in enumerate(grid): for col_idx, _ in enumerate(row): blocked = 1 if knowledge_base[row_idx][col_idx].status == CellState.BLOCKED else 0 current_grid[row_idx][col_idx] = blocked path, _, _, _, _ = a_star(current_grid, source, goal) if path == []: solvable = False for path_idx, (row_idx, col_idx) in enumerate(path): ran_inference = False if not knowledge_base[row_idx][col_idx].visited: if grid[row_idx][col_idx] == 0: knowledge_base[row_idx][col_idx].C = \ _get_num_blocked_neighbors(grid, row_idx, col_idx) knowledge_base[row_idx][col_idx].visited = True if knowledge_base[row_idx][col_idx].status == CellState.UNKNOWN: cell_state = \ CellState.BLOCKED if grid[row_idx][col_idx] == 1 else CellState.OPEN update_cell_status(cell_state, knowledge_base, row_idx, col_idx, grid_len) run_inference(grid_len, knowledge_base, inference) ran_inference = True full_path_and_blocks.append((row_idx, col_idx)) # If we hit a block, restart and try again. Otherwise, continue if grid[row_idx][col_idx] == 1 and full_path != []: num_bumps += 1 source = full_path[-1] break # Only add to the path if we aren't at the source # (otherwise we would have duplicates on restarts) if (row_idx, col_idx) != source: if dataformat: if dataformat == 1: source_and_neighbors = [] for i, (dx, dy) in enumerate([(0,0), (0,1), (1,0), (0,-1), (-1,0), (1,1), (-1,-1), (1,-1), (-1,1)]): new_x, new_y = [row_idx + dx, col_idx + dy] if 0 <= new_x < n and 0 <= new_y < m: source_and_neighbors.extend([new_x, new_y, *(Intel.convert(knowledge_base[new_x][new_y]))]) else: source_and_neighbors.extend([new_x, new_y, *([0] * INTEL_SIZE)]) grid_states.append(source_and_neighbors) elif dataformat == 2: grid_state = np.empty(shape=(len(grid), len(grid[0]), INTEL_SIZE)) for i in range(len(grid)): for j in range(len(grid[0])): for k,v in enumerate(Intel.convert(knowledge_base[i][j])): grid_state[i][j][k] = v grid_states.append(grid_state) grid_sources.append((source)) actions.append(int(_get_cardinal_direction(*source, row_idx, col_idx))) full_path.append((row_idx, col_idx)) # If there is a block in our current path, but the current cell was not blocked if ran_inference and any(knowledge_base[x][y].status == CellState.BLOCKED for (x,y) in path[path_idx+1:]): source = full_path[-1] break if solvable: # Create current grid based on current knowledge base current_grid = np.zeros((len(grid), len(grid[0]))) for row_idx, row in enumerate(grid): for col_idx, _ in enumerate(row): blocked = 1 if knowledge_base[row_idx][col_idx].status == CellState.BLOCKED or not knowledge_base[row_idx][col_idx].visited else 0 current_grid[row_idx][col_idx] = blocked shortest_path, _, _, _, _ = a_star(current_grid, (0,0), goal) return shortest_path, len(shortest_path), grid_states, grid_sources, actions, full_path, len(full_path), num_bumps, full_path_and_blocks return [], 0, grid_states, grid_sources, actions, actions, full_path, len(full_path), num_bumps, full_path_and_blocks def create_initial_kb(grid: List[List[int]]) -> List[List[Intel]]: kb = np.zeros((len(grid), len(grid[0]))) kb = kb.astype('object') for row_idx, row in enumerate(grid): for col_idx, _ in enumerate(row): N = len(_get_neighbor_indices(len(grid), row_idx, col_idx)) kb[row_idx][col_idx] = Intel(N) return kb def run_inference( grid_len: int, kb: List[List[Intel]], single_inference_func : Callable = _run_single_inference_agent3 ): changed = True while changed: changed = single_inference_func(grid_len, kb) def update_cell_status( new_state: CellState, kb: List[List[Intel]], row_idx: int, col_idx: int, grid_len: int ) -> None: # Check if the status can be changed if kb[row_idx][col_idx].status != CellState.UNKNOWN: return if new_state == CellState.UNKNOWN: raise Exception("Cell state to change to cannot be unknown") kb[row_idx][col_idx].status = new_state # If we change the status, change neighbor info neighbors = _get_neighbor_indices(grid_len, row_idx, col_idx) for (row, col) in neighbors: kb[row][col].H -= 1 if new_state == CellState.BLOCKED: kb[row][col].B += 1 elif new_state == CellState.OPEN: kb[row][col].E += 1 def _get_num_blocked_neighbors( grid: List[List[int]], row_idx: int, col_idx: int ) -> int: num_blocked = 0 neighbors = _get_neighbor_indices(len(grid), row_idx, col_idx) for row, col in neighbors: if grid[row][col] == 1: num_blocked += 1 return num_blocked def _get_neighbor_indices(grid_len: int, row_idx: int, col_idx: int) -> List[Tuple[int, int]]: neighbors = [] for row in range(row_idx - 1, row_idx + 2): if row < 0 or row >= grid_len: continue for col in range(col_idx - 1, col_idx + 2): if row_idx == row and col_idx == col: continue elif col < 0 or col >= grid_len: continue neighbors.append((row, col)) return neighbors

NUM_OF_MAZES_PER_TEST = 30 NUM_TRAINING_MAZES = 50 NUM_TESTING_MAZES = 50 MAZE_DIM = 50 DENSITY = 0.3 SOLVABILITY = True IS_TERRAIN_ENABLED = False source = (0,0) target = (MAZE_DIM-1, MAZE_DIM-1) # grid, _, _ = gen_gridworld(dim, density, solvability, is_terrain_enabled, source, target) # discovered_path_3, length_discovered_path_3, grid_states, grid_sources, actions, full_path_3, trajectory_3, num_bumps_3 = agent3(grid, 2) # print(f"Agent 3: { length_discovered_path_3}, trajectory: {trajectory_3}, Num of walls bumped into: {num_bumps_3}") # # pretty_print_explored_region(grid, full_path_3, discovered_path_3) # print(len(grid_states)) #, grid_states) # print(len(actions)) #, actions)

def gen_grid_data_agent3(num_iter, agent, arg=None): all_grids = [] all_sources = [] all_actions = [] for i in range(num_iter): if i % 100 == 0: print("Maze", i) grid, _, _ = gen_gridworld(MAZE_DIM, DENSITY, SOLVABILITY, IS_TERRAIN_ENABLED, source, target) if arg: explored_grid, path, grid_states, grid_sources, actions, len_path, visited_cells, *_ = agent(grid, arg) else: explored_grid, path, grid_states, grid_sources, actions, len_path, visited_cells, *_ = agent(grid) all_grids.extend(grid_states) all_sources.extend(sources) all_actions.extend(actions) return (np.array(all_grids), np.array(all_sources), np.array(all_actions))

# Model Building def create_p2_dense_model(): source_input = tf.keras.layers.Input( shape = (81) ) dense_1 = tf.keras.layers.Dense( units = 2000, activation = tf.nn.relu )( source_input ) dense_2 = tf.keras.layers.Dense( units = 2000, activation = tf.nn.relu )( dense_1 ) dense_3 = tf.keras.layers.Dense( units = 2000, activation = tf.nn.relu )( dense_2 ) probabilities = tf.keras.layers.Dense( units = 4, activation = tf.nn.softmax )( dense_3 ) model = tf.keras.Model(inputs=[source_input], outputs=probabilities) model.compile( optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'] ) print(model.summary()) return model

# Training def training_p2_dense(): BATCH_SIZE = 25 NUM_TRAINING_MAZES = 250 total_grids = [] total_sources = [] total_actions = [] for i in range(0, NUM_TRAINING_MAZES, BATCH_SIZE): print(f"Training starting on maze {i}") total_grids, total_sources, total_actions = gen_grid_data_agent3(BATCH_SIZE, agent3, 1) print(np.shape(total_grids), np.shape(total_sources), np.shape(total_actions)) history = model.fit(total_grids, total_actions, epochs = 3, validation_split=.1)

# Testing model_dense_p2 = tf.keras.models.load_model('./test_data_dense_p2/')

# Hyperparameter Tuning def create_tuning_model(): def build_model(hp): source_input = tf.keras.layers.Input( shape = (2) ) # Grid input grid_input = tf.keras.layers.Input(shape=(MAZE_DIM, MAZE_DIM, INTEL_SIZE)) i = hp.Choice('kernel_and_stride_size', values=[2,3]) conv2d_1 = tf.keras.layers.Conv2D(filters=hp.Int("filters1", min_value=256, max_value=2048, step=128), kernel_size=(i,i), strides=(i,i), padding="same",activation="relu")(grid_input) maxpool2d_1 = tf.keras.layers.MaxPooling2D(pool_size=(i, i), padding='same')( conv2d_1 ) conv2d_2 = tf.keras.layers.Conv2D(filters=hp.Int("filters2", min_value=32, max_value=256, step=64),kernel_size=i,strides=(i,i), padding="same",activation="relu")( maxpool2d_1 ) maxpool2d_2 = tf.keras.layers.MaxPooling2D(pool_size=(i, i), padding='same')( conv2d_2 ) conv2d_3 = tf.keras.layers.Conv2D(filters=hp.Int("filters3", min_value=4, max_value=32, step=4),kernel_size=i, strides=(i,i), padding="same",activation="relu")( maxpool2d_2 ) maxpool2d_3 = tf.keras.layers.MaxPooling2D(pool_size=(i, i), padding='same')( conv2d_3 ) flatten = tf.keras.layers.Flatten()( maxpool2d_3 ) grids_and_sources = tf.keras.layers.Concatenate()([flatten, source_input]) dense_1 = tf.keras.layers.Dense(units=hp.Int("units", min_value=1000, max_value=3000, step=500), activation='relu')(grids_and_sources) dense_2 = tf.keras.layers.Dense(units = 500, activation = hp.Choice('activation function', values=['relu', 'sigmoid']))( dense_1) logits = tf.keras.layers.Dense(units = 4, activation = "softmax" )( dense_2 ) model = tf.keras.Model(inputs=[grid_input, source_input], outputs=logits) model.compile( optimizer = keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2,1e-4])), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy']) return model tuner = RandomSearch( build_model, objective='val_accuracy', max_trials=10, executions_per_trial=1, # directory ) tuner.search_space_summary() # tuner.search((train_total_grids, train_total_sources), train_total_actions, epochs=5, validation_data=((test_total_grids, test_total_sources), test_total_actions)) tuner.results_summary() models = tuner.get_best_models(num_models=2) best_model = models[0] print(best_model.summary()) return best_model

def create_p2_cnn_model(): source_input = tf.keras.layers.Input( shape = (2) ) # Grid input grid_input = tf.keras.layers.Input(shape=(MAZE_DIM, MAZE_DIM, INTEL_SIZE)) conv2d_1 = tf.keras.layers.Conv2D(filters=256,kernel_size=2,padding="same",activation="relu")(grid_input) conv2d_11 = tf.keras.layers.Conv2D(filters=128,kernel_size=2,padding="same",activation="relu")(conv2d_1) conv2d_12 = tf.keras.layers.Conv2D(filters=64,kernel_size=2,padding="same",activation="relu")(conv2d_11) conv2d_13 = tf.keras.layers.Conv2D(filters=32,kernel_size=2,padding="same",activation="relu")( conv2d_12 ) conv2d_3 = tf.keras.layers.Conv2D(filters=4,kernel_size=2,padding="same",activation="relu")(conv2d_13 ) flatten = tf.keras.layers.Flatten()( conv2d_3 ) grids_and_sources = tf.keras.layers.Concatenate()([flatten, source_input]) dense_1 = tf.keras.layers.Dense( units = 2500, activation = tf.nn.relu )( grids_and_sources ) dense_2 = tf.keras.layers.Dense( units = 500, activation = tf.nn.relu )( dense_1) logits = tf.keras.layers.Dense( units = 4, activation = "softmax" )( dense_2 ) cnn_model = tf.keras.Model(inputs=[grid_input, source_input], outputs=logits) cnn_model.compile( optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'] ) print(cnn_model.summary()) return cnn_model

# Training def training_p2_cnn(): BATCH_SIZE = 10 NUM_TRAINING_MAZES = 250 total_grids = [] total_sources = [] total_actions = [] for i in range(0, NUM_TRAINING_MAZES, BATCH_SIZE): print(f"Training starting on maze {i}") total_grids, total_sources, total_actions = gen_grid_data_agent3(BATCH_SIZE, agent3, 2) print(np.shape(total_grids), np.shape(total_sources), np.shape(total_actions)) history = model.fit([total_grids,total_sources], total_actions, epochs = 3, validation_split=.1)

model_cnn_p2 = tf.keras.models.load_model('./test_data_cnn_p2_regular/')

from random import choice from timeit import default_timer as timer import tensorflow as tf import numpy as np action_movement_dict = { 0 : (-1,0), # North 1 : (0, 1), # East 2 : (1, 0), # South 3 : (0, -1) # West } FRAME_SIZE = 1 def run_nn_agent_agent3(complete_grid, model, full_path, data_func=None): knowledge_base = create_initial_kb(complete_grid) grid_len = len(complete_grid) n, m = grid_len-1, grid_len-1 original_source = (0,0) source, goal = (0, 0), (n,m) # full_path = [source] path = [] solvable = True num_bumps = trajectory = num_actions = miss = correct = 0 # movement_grid = [[-1]*m for _ in range(n)] rounds = 0 # REMOVE ME while source != goal: rounds += 1 # Generate Current grid from Knowledge base grid = np.zeros((len(complete_grid), len(complete_grid[0]))) for row_idx, row in enumerate(complete_grid): for col_idx, _ in enumerate(row): blocked = 1 if knowledge_base[row_idx][col_idx].status == CellState.BLOCKED else 0 grid[row_idx][col_idx] = blocked # Update KB row_idx, col_idx = source if not knowledge_base[row_idx][col_idx].visited: if complete_grid[row_idx][col_idx] == 0: knowledge_base[row_idx][col_idx].C = _get_num_blocked_neighbors(grid, row_idx, col_idx) knowledge_base[row_idx][col_idx].visited = True if knowledge_base[row_idx][col_idx].status == CellState.UNKNOWN: cell_state = CellState.BLOCKED if complete_grid[row_idx][col_idx] == 1 else CellState.OPEN update_cell_status(cell_state, knowledge_base, row_idx, col_idx, grid_len) if cell_state == CellState.BLOCKED: source = prev run_inference(grid_len, knowledge_base, _run_single_inference_agent3) # Dense if data_func == 1: grid_state = [] for i, (dx, dy) in enumerate([(0,0), (0,1), (1,0), (0,-1), (-1,0), (1,1), (-1,-1), (1,-1), (-1,1)]): new_x, new_y = [row_idx + dx, col_idx + dy] if 0 <= new_x < n and 0 <= new_y < m: grid_state.extend([new_x, new_y, *(Intel.convert(knowledge_base[new_x][new_y]))]) else: grid_state.extend([new_x, new_y, *([0] * INTEL_SIZE)]) input_data = np.array([grid_state]) # didn't use source for input data model # CNN elif data_func == 2 or data_func == 3: grid_state = np.empty(shape=(len(grid), len(grid[0]), INTEL_SIZE)) for i in range(len(grid)): for j in range(len(grid[0])): for k,v in enumerate(Intel.convert(knowledge_base[i][j])): grid_state[i][j][k] = v if data_func == 3: grid_state = tf.reshape(grid_state, (FRAME_SIZE, MAZE_DIM, MAZE_DIM, INTEL_SIZE)) input_data = (np.array([grid_state]), np.array([source])) ### Prediction ### out = model(input_data).numpy() prediction = np.argmax(out, axis = 1)[0] # num_actions += 1 # If attempted action is not valid for some reason, pick a random one that is # Also, if we're attempting to go the same way we already have (in a loop), run a_star to get out of the loop action = action_movement_dict[prediction] x, y = (source[0] + action[0], source[1] + action[1]) if (x,y) != full_path[rounds]: miss += 1 (x,y) = full_path[rounds] else: correct += 1 grid[x][y] = complete_grid[x][y] trajectory += 1 prev = source source = (x, y) if grid[x][y] == 1: continue path.append(source) # trajectory = correct + miss return path, trajectory, correct, miss

# due to how slow this runs only 10 (trials) x 10 (mazes) was done nn_means_path = [175.4, 175.5, 169.2, 160.8, 153.5, 158.0, 163.8, 160.4, 171.0, 166.7] nn_means_traj = [322.2, 312.8, 311.0, 281.0, 272.0, 283.1, 295.0, 287.6, 329.9, 303.4] nn_means_times =[3.8288494416963657, 3.524037164998299, 3.4668822037012434, 3.129986681700393, 3.169014308699116, 3.3250332264986353, 4.867095653699653, 4.109915263901348, 3.8719698475979385, 4.09942249039741] nn_means_corrects =[82.5, 77.8, 75.1, 69.0, 67.5, 71.6, 72.0, 69.9, 82.0, 74.1] nn_means_misses = [239.7, 235.0, 235.9, 212.0, 204.5, 211.5, 223.0, 217.7, 247.9, 229.3] cnn_means_path = [175.4, 175.5, 169.2, 160.8, 153.5, 158.0, 163.8, 160.4, 171.0, 166.7] cnn_means_traj = [322.2, 312.8, 311.0, 281.0, 272.0, 283.1, 295.0, 287.6, 329.9, 303.4] cnn_means_times =[31.878190686796735, 30.192351302296448, 29.74190882630064, 27.12558812719799, 26.970018435301608, 27.708891010099613, 45.04423980590072, 34.643869927999916, 33.82419218010182, 35.71657393649948] cnn_means_corrects = [89.6, 87.7, 87.9, 80.0, 77.3, 80.2, 83.7, 81.0, 94.2, 84.4] cnn_means_misses = [232.6, 225.1, 223.1, 201.0, 194.7, 202.9, 211.3, 206.6, 235.7, 219.0] a_means_path = [124.6, 138.4, 131.6, 128.0, 127.2, 121.6, 127.8, 126.6, 129.8, 128.6] a_means_traj = [209.6, 204.2, 197.0, 182.6, 173.6, 182.8, 188.0, 183.8, 203.8, 192.8] a_means_times = [0.41573393699800365, 0.3616427813001792, 0.3975740644018515, 0.32227378389798106, 0.3559665934983059, 0.338859436500934, 0.6192624611008796, 0.45207660400046734, 0.4427161724001053, 0.4525190754982759] a_means_corrects = a_means_traj a_means_misses = [0] * len(a_means_traj)

import matplotlib.pyplot as plt agents = ["NN", "CNN", "A Star"] paths = [nn_means_path, cnn_means_path, a_means_path] for path in paths: idx = list(range(len(path))) plt.plot(idx, path) plt.legend(["NN", "CNN", "A Star"]) plt.xlabel("Agents") plt.ylabel("Average Path") plt.title("Average Path For Each Agent") plt.grid() plt.show()

agents = ["NN", "CNN", "A Star"] trajs = [nn_means_traj, cnn_means_traj, a_means_traj] for traj in trajs: idx = list(range(len(traj))) plt.plot(idx, traj) plt.legend(agents) plt.xlabel("Agents") plt.ylabel("Average Trajectory") plt.title("Average Trajectory For Each Agent") plt.grid() plt.show()

agents = ["NN", "CNN", "A Star"] times = [nn_means_times, cnn_means_times, a_means_times] for time in times: idx = list(range(len(time))) plt.plot(idx, time) plt.legend(agents) plt.xlabel("Agents") plt.ylabel("Average Time") plt.title("Average Time For Each Agent") plt.grid() plt.show()

agents = ["NN", "CNN", "A Star"] corrects = [nn_means_corrects, cnn_means_corrects, a_means_corrects] for correct in corrects: idx = list(range(len(correct))) plt.plot(idx, correct) plt.legend(agents) plt.xlabel("Agents") plt.ylabel("Average Corrects") plt.title("Average Corrects For Each Agent") plt.grid() plt.show()

agents = ["NN", "CNN", "A Star"] misses = [nn_means_misses, cnn_means_misses, a_means_misses] for miss in misses: idx = list(range(len(miss))) plt.plot(idx, miss) plt.legend(agents) plt.xlabel("Agents") plt.ylabel("Average Misses") plt.title("Average Misses For Each Agent") plt.grid() plt.show()

def run_nn_agent_agent3_v2(complete_grid, model): original_source = (0,0) # we need to remember the original_source node bc in repeated A* source changes source = original_source n, m = (len(complete_grid), len(complete_grid[0])) goal = (n-1, m-1) # dimensions of grid num_actions = trajectory = 0 complete_grid = np.array(complete_grid) grid = np.array([[0.5]*(m) for _ in range(n)]) grid[source[0]][source[1]] = 0.0 path = [source] grid_states = [] movement_grid = [[-1]*m for _ in range(n)] while source != goal: # execution step valid_actions = [] # Generate input data for agent - grid, source + neighbors tuple source_neighbors_data = [source[0], source[1]] for dx, dy in [(0, 1), (-1, 0), (0, -1), (1, 0)]: # generate children new_x, new_y = (source[0] + dx, source[1] + dy) if 0 <= new_x < len(grid) and 0 <= new_y < len(grid): source_neighbors_data.extend([new_x, new_y, grid[new_x][new_y]]) if grid[new_x][new_y] != 1: valid_actions.append((dx, dy)) else: source_neighbors_data.extend([0,0,1]) input_data = (np.array([grid]), np.array([source_neighbors_data])) # Give agent grid and (try to) take step by agent out = model(input_data).numpy() prediction = np.argmax( out , axis = 1 )[0] # Move to predicted state, if possible num_actions += 1 action_movement_dict = { 0 : (-1,0), # North 1 : (0, 1), # East 2 : (1, 0), # South 3 : (0, -1) # West } # If attempted action is not valid for some reason, pick a random one that is # Also, if we're attempting to go the same way we already have (in a loop), run a_star to get out of the loop action = action_movement_dict[prediction] if action not in valid_actions: action = choice(valid_actions) elif movement_grid[source[0]][source[1]] == prediction: valid_actions.remove(action) a_star_path, *_ = a_star(deepcopy(grid), source, goal) idx = 1 next = a_star_path[0] # Follow a_star until we hit a block or we are no longer in the loop while source in path: if idx >= len(a_star_path): # print("Finished here") break curr = a_star_path[idx] x, y = curr grid[x][y] = complete_grid[x][y] if grid[x][y] == 1: # If we hit a new block, break break # Move from source to curr trajectory += 1 source = (x, y) path.append(source) idx += 1 continue movement_grid[source[0]][source[1]] = prediction # Move to new cell x, y = (source[0] + action[0], source[1] + action[1]) grid[x][y] = complete_grid[x][y] if grid[x][y] == 1: continue trajectory += 1 source = (x, y) path.append(source) # print(f"current path: {path[-10:]}") return path, trajectory, num_actions def _run_single_inference_agent3(grid_len: int, kb: List[List[Intel]]) -> bool: changed = False for row_idx, row in enumerate(kb): for col_idx, intel in enumerate(row): # If the cell has not been visited yet, we can't infer anything about its neighbors # since C has not been set # If Hx=0: nothing remains to be inferred about cell x. if not intel.visited or intel.H == 0: continue # If Cx=Bx: all remaining hidden neighbors of x are empty. if intel.C == intel.B: for (n_row, n_col) in _get_neighbor_indices(grid_len, row_idx, col_idx): if kb[n_row][n_col].status == CellState.UNKNOWN: update_cell_status(CellState.OPEN, kb, n_row, n_col, grid_len) changed = True # If Nx−Cx=Ex: all remaining hidden neighbors of x are blocked. elif intel.N - intel.C == intel.E: for (n_row, n_col) in _get_neighbor_indices(grid_len, row_idx, col_idx): if kb[n_row][n_col].status == CellState.UNKNOWN: update_cell_status(CellState.BLOCKED, kb, n_row, n_col, grid_len) changed = True return changed def agent3_nn(grid: List[List[int]], inference: Callable = _run_single_inference_agent3): """ Args: Returns: list(tuple(int, int)): the path from the source node to the goal node which consists of coordinate points (i.e. x,y) in the form of tuples. int: the length of the discovered path from source to goal (i.e. the length of the first return value) list(tuple(int,int)): all visited squares from the source point. Contains int tuples in the form of (x,y). int: trajectory - num of moves the agent makes int: num of times the agent assumes wrong and enters a blocked square """ knowledge_base = create_initial_kb(grid) grid_len = len(grid) source, goal = (0, 0), (grid_len - 1, grid_len - 1) full_path = [source] solvable = True num_bumps = 0 seen = defaultdict(int) while goal != full_path[-1]: # Create current grid based on current knowledge base current_grid = [] for row_idx, row in enumerate(grid): current_grid.append([]) for col_idx, _ in enumerate(row): blocked = 1 if knowledge_base[row_idx][col_idx].status == CellState.BLOCKED else 0 current_grid[row_idx].append(blocked) path, _, _, _, _ = a_star(current_grid, manhattan_heuristic, source) for path_idx, (row_idx, col_idx) in enumerate(path): ran_inference = False if not knowledge_base[row_idx][col_idx].visited: if grid[row_idx][col_idx] == 0: knowledge_base[row_idx][col_idx].C = \ _get_num_blocked_neighbors(grid, row_idx, col_idx) knowledge_base[row_idx][col_idx].visited = True if knowledge_base[row_idx][col_idx].status == CellState.UNKNOWN: cell_state = \ CellState.BLOCKED if grid[row_idx][col_idx] == 1 else CellState.OPEN update_cell_status(cell_state, knowledge_base, row_idx, col_idx, grid_len) run_inference(grid_len, knowledge_base, inference) ran_inference = True # If we hit a block, restart and try again. Otherwise, continue if grid[row_idx][col_idx] == 1 and full_path != []: num_bumps += 1 source = full_path[-1] break # Only add to the path if we aren't at the source # (otherwise we would have duplicates on restarts) if (row_idx, col_idx) != source: full_path.append((row_idx, col_idx)) # If there is a block in our current path, but the current cell was not blocked if ran_inference and any(knowledge_base[x][y].status == CellState.BLOCKED for (x,y) in path[path_idx+1:]): source = full_path[-1] break if solvable: # Create current grid based on current knowledge base current_grid = [] for row_idx, row in enumerate(grid): current_grid.append([]) for col_idx, _ in enumerate(row): blocked = 1 if knowledge_base[row_idx][col_idx].status == CellState.BLOCKED or not knowledge_base[row_idx][col_idx].visited else 0 current_grid[row_idx].append(blocked) shortest_path, _, _, _, _ = a_star(current_grid, manhattan_heuristic, (0,0)) return shortest_path, len(shortest_path), full_path, len(full_path), num_bumps return [], 0, full_path, len(full_path), num_bumps def create_initial_kb(grid: List[List[int]]) -> List[List[Intel]]: kb = [] for row_idx, row in enumerate(grid): kb.append([]) for col_idx, _ in enumerate(row): N = len(_get_neighbor_indices(len(grid), row_idx, col_idx)) kb[row_idx].append(Intel(N)) return kb def run_inference( grid_len: int, kb: List[List[Intel]], single_inference_func : Callable = _run_single_inference_agent3 ): changed = True while changed: changed = single_inference_func(grid_len, kb) def update_cell_status( new_state: CellState, kb: List[List[Intel]], row_idx: int, col_idx: int, grid_len: int ) -> None: # Check if the status can be changed if kb[row_idx][col_idx].status != CellState.UNKNOWN: return if new_state == CellState.UNKNOWN: raise Exception("Cell state to change to cannot be unknown") kb[row_idx][col_idx].status = new_state # If we change the status, change neighbor info neighbors = _get_neighbor_indices(grid_len, row_idx, col_idx) for (row, col) in neighbors: kb[row][col].H -= 1 if new_state == CellState.BLOCKED: kb[row][col].B += 1 elif new_state == CellState.OPEN: kb[row][col].E += 1 def _get_num_blocked_neighbors( grid: List[List[int]], row_idx: int, col_idx: int ) -> int: num_blocked = 0 neighbors = _get_neighbor_indices(len(grid), row_idx, col_idx) for row, col in neighbors: if grid[row][col] == 1: num_blocked += 1 return num_blocked def _get_neighbor_indices(grid_len: int, row_idx: int, col_idx: int) -> List[Tuple[int, int]]: neighbors = [] for row in range(row_idx - 1, row_idx + 2): if row < 0 or row >= grid_len: continue for col in range(col_idx - 1, col_idx + 2): if row_idx == row and col_idx == col: continue elif col < 0 or col >= grid_len: continue neighbors.append((row, col)) return neighbors

# CNN w/ Max Pooling # Model Building def create_p2_cnn_model(): # Source input source_input = tf.keras.layers.Input( shape = (2) ) # Grid input grid_input = tf.keras.layers.Input(shape=(MAZE_DIM, MAZE_DIM, INTEL_SIZE)) conv2d_1 = tf.keras.layers.Conv2D(filters=256,kernel_size=2,padding="same",activation="relu")(grid_input) maxpool2d_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_1 ) conv2d_11 = tf.keras.layers.Conv2D(filters=128,kernel_size=2,padding="same",activation="relu")(maxpool2d_1) maxpool2d_11 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_11 ) conv2d_12 = tf.keras.layers.Conv2D(filters=64,kernel_size=2,padding="same",activation="relu")(maxpool2d_11) maxpool2d_12 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_12 ) conv2d_13 = tf.keras.layers.Conv2D(filters=32,kernel_size=2,padding="same",activation="relu")( maxpool2d_12 ) maxpool2d_13 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_13 ) conv2d_3 = tf.keras.layers.Conv2D(filters=4,kernel_size=2,padding="same",activation="relu")( maxpool2d_13 ) maxpool2d_3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_3 ) flatten = tf.keras.layers.Flatten()( maxpool2d_3 ) grids_and_sources = tf.keras.layers.Concatenate()([flatten, source_input]) dense_1 = tf.keras.layers.Dense( units = 2500, activation = tf.nn.relu )( grids_and_sources ) dense_2 = tf.keras.layers.Dense( units = 500, activation = tf.nn.relu )( dense_1) logits = tf.keras.layers.Dense( units = 4, activation = "softmax" )( dense_2 ) model = tf.keras.Model(inputs=[grid_input, source_input], outputs=logits) model.compile( optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'] ) print(model.summary()) return model

# RNN FRAME_SIZE = 1 def creating_rnn_p2(): # Source Input source_input = tf.keras.layers.Input(shape=(2)) # Grid Input grid_input = tf.keras.layers.Input(shape=(FRAME_SIZE, MAZE_DIM, MAZE_DIM, INTEL_SIZE)) conv2d_lstm_1 = tf.keras.layers.ConvLSTM2D(filters=128, kernel_size=2, padding="same", return_sequences=True)(grid_input) batchNormalization_1 = tf.keras.layers.BatchNormalization()(conv2d_lstm_1) conv2d_lstm_2 = tf.keras.layers.ConvLSTM2D(filters=64, kernel_size=2, padding="same", return_sequences=True)(batchNormalization_1) batchNormalization_2 = tf.keras.layers.BatchNormalization()(conv2d_lstm_2 ) conv2d_lstm_3 = tf.keras.layers.ConvLSTM2D(filters=32, kernel_size=2, padding="same", return_sequences=True)(batchNormalization_2) batchNormalization_3 = tf.keras.layers.BatchNormalization()(conv2d_lstm_3) conv2d_lstm_4 = tf.keras.layers.ConvLSTM2D(filters=16, kernel_size=2, padding="same", return_sequences=True)(batchNormalization_3) batchNormalization_4 = tf.keras.layers.BatchNormalization()(conv2d_lstm_4) flatten = tf.keras.layers.Flatten()(batchNormalization_4) grids_and_sources = tf.keras.layers.Concatenate()([flatten, source_input]) dense_1 = tf.keras.layers.Dense( units = 100, activation = tf.nn.relu )( grids_and_sources ) logits = tf.keras.layers.Dense( units = 4, activation = "softmax" )( dense_1 ) cnn_model = tf.keras.Model(inputs=[grid_input, source_input], outputs=logits) cnn_model.compile( optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'] ) print(cnn_model.summary()) return cnn_model

## Testing def run_iteration_bonus(i=0): rnn_path_lens = [] rnn_trajs = [] rnn_times = [] rnn_corrects = [] rnn_misses = [] cnn_max_path_lens = [] cnn_max_trajs = [] cnn_max_times = [] cnn_max_corrects = [] cnn_max_misses = [] agent_path_lens = [] agent_trajs = [] agent_times = [] # a_corrects = [] # a_misses = [] print(f"starting iteration {i}") for j in range(NUM_TRIALS): print(f"trial {(i, j)}") grid, _, _ = gen_gridworld(MAZE_DIM, DENSITY, SOLVABILITY, IS_TERRAIN_ENABLED, source, target) start = timer() shortest_path, len_path, _, _, _, full_path, traj, _, full_path_and_blocks = agent3(grid) stop = timer() agent_star_time = stop - start agent_path_lens.append(len_path) agent_trajs.append(traj) agent_times.append(agent_star_time) start = timer() path, traj, corrects, misses = run_nn_agent_agent3(grid, p2_rnn_model, full_path_and_blocks, 3) stop = timer() rnn_time = stop - start rnn_path_lens.append(len(set(path))) rnn_trajs.append(traj) rnn_times.append(rnn_time) rnn_corrects.append(corrects) rnn_misses.append(misses) start = timer() path, traj, corrects, misses = run_nn_agent_agent3(grid, p2_cnn_model_ec, full_path_and_blocks ,2) stop = timer() cnn_max_time = stop - start cnn_max_path_lens.append(len(set(path))) cnn_max_trajs.append(traj) cnn_max_times.append(cnn_max_time) cnn_max_corrects.append(corrects) cnn_max_misses.append(misses) print("RNN Path", rnn_path_lens) print("RNN Traj", rnn_trajs) print("RNN Time", rnn_times) print("RNN Corrects", rnn_corrects) print("RNN Misses", rnn_misses) print("CNN Maxpool Path", cnn_max_path_lens) print("CNN Maxpool Traj", cnn_max_trajs) print("CNN Maxpool Time", cnn_max_times) print("CNN Maxpool Corrects", cnn_max_corrects) print("CNN Maxpool Misses", cnn_max_misses) print("Agent Path", agent_path_lens) print("Agent Traj", agent_trajs) print("Agent Time", agent_times) # print("Agent Corrects", agent_corrects) # print("Agent Misses", agent_misses) print("") rnn_means_path.append(sum(rnn_path_lens)/NUM_TRIALS) rnn_means_traj.append(sum(rnn_trajs)/NUM_TRIALS) rnn_means_times.append(sum(rnn_times)/NUM_TRIALS) rnn_means_corrects.append(sum(rnn_corrects)/NUM_TRIALS) rnn_means_misses.append(sum(rnn_misses)/NUM_TRIALS) cnn_max_means_path.append(sum(cnn_max_path_lens)/NUM_TRIALS) cnn_max_means_traj.append(sum(cnn_max_trajs)/NUM_TRIALS) cnn_max_means_times.append(sum(cnn_max_times)/NUM_TRIALS) cnn_max_means_corrects.append(sum(cnn_max_corrects)/NUM_TRIALS) cnn_max_means_misses.append(sum(cnn_max_misses)/NUM_TRIALS) agent_means_path.append(sum(agent_path_lens)/NUM_TRIALS) agent_means_traj.append(sum(agent_trajs)/NUM_TRIALS) agent_means_times.append(sum(agent_times)/NUM_TRIALS) # a_means_corrects.append(sum(a_corrects)/NUM_TRIALS) # a_means_misses.append(sum(a_misses)/NUM_TRIALS) rnn_means_path = [] rnn_means_traj = [] rnn_means_times = [] rnn_means_corrects = [] rnn_means_misses = [] cnn_max_means_path = [] cnn_max_means_traj = [] cnn_max_means_times = [] cnn_max_means_corrects = [] cnn_max_means_misses = [] agent_means_path = [] agent_means_traj = [] agent_means_times = [] agent_means_corrects = [] agent_means_misses = [] p2_rnn_model = tf.keras.models.load_model('./test_data_rnn_p2/') p2_cnn_model_ec = tf.keras.models.load_model('./test_data_cnn_p2_ec') NUM_ITERATIONS = 5 #30 NUM_TRIALS = 5 #50 MAZE_DIM = 50 DENSITY = 0.3 SOLVABILITY = True IS_TERRAIN_ENABLED = False source = (0,0) target = (MAZE_DIM-1, MAZE_DIM-1) for i in range(NUM_ITERATIONS): run_iteration_bonus(i) agent_means_corrects = agent_means_traj agent_means_misses = [0] * len(agent_means_traj) # print(rnn_means_path,rnn_means_traj,rnn_means_times,rnn_means_corrects,rnn_means_misses,cnn_max_means_path,cnn_max_means_traj,cnn_max_means_times,cnn_max_means_corrects,cnn_max_means_misses,agent_means_path,agent_means_traj,agent_means_times,agent_means_corrects,agent_means_misses)

# due to how long run the simulations take we were only able to do 5 (trials) x 5 (mazes) agents = ["RNN", "CNN Maxpool", "A Star"] paths = [rnn_means_path, cnn_max_means_path, agent_means_path] for path in paths: idx = list(range(len(path))) plt.plot(idx, path) plt.legend(agents) plt.xlabel("Agents") plt.ylabel("Average Path") plt.title("Average Path For Each Agent") plt.grid() plt.show()

agents = ["RNN", "CNN Maxpool", "A Star"] trajs = [rnn_means_traj, cnn_max_means_traj, agent_means_traj] for traj in trajs: idx = list(range(len(traj))) plt.plot(idx, traj) plt.legend(agents) plt.xlabel("Agents") plt.ylabel("Average Trajectory") plt.title("Average Trajectory For Each Agent") plt.grid() plt.show()

agents = ["RNN", "CNN Maxpool", "A Star"] times = [rnn_means_times, cnn_max_means_times, agent_means_times] for time in times: idx = list(range(len(time))) plt.plot(idx, time) plt.legend(agents) plt.xlabel("Agents") plt.ylabel("Average Time") plt.title("Average Time For Each Agent") plt.grid() plt.show()

agents = ["RNN", "CNN Maxpool", "A Star"] corrects = [rnn_means_corrects, cnn_max_means_corrects, agent_means_corrects] for correct in corrects: idx = list(range(len(correct))) plt.plot(idx, correct) plt.legend(agents) plt.xlabel("Agents") plt.ylabel("Average Corrects") plt.title("Average Corrects For Each Agent") plt.grid() plt.show()

agents = ["RNN", "CNN Maxpool", "A Star"] misses = [rnn_means_misses, cnn_max_means_misses, agent_means_misses] for miss in misses: idx = list(range(len(miss))) plt.plot(idx, miss) plt.legend(agents) plt.xlabel("Agents") plt.ylabel("Average Misses") plt.title("Average Misses For Each Agent") plt.grid() plt.show()

import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.optimizers import Adam def build_model(states, actions): # CREATE NN MODEL grid_input = tf.keras.layers.Input( shape = (MAZE_DIM, MAZE_DIM) ) flatten_grids = tf.keras.layers.Flatten()( grid_input ) # Source input source_input = tf.keras.layers.Input( shape = (14) ) # Full input grids_and_sources = tf.keras.layers.Concatenate()([flatten_grids, source_input]) dense_1 = tf.keras.layers.Dense( units = 2000, activation = tf.nn.relu )( grids_and_sources ) dense_2 = tf.keras.layers.Dense( units = 500, activation = tf.nn.relu )( dense_1 ) probabilities = tf.keras.layers.Dense( units = 4, activation = tf.nn.linear )( dense_2 ) return model model = build_model(states, actions) model.summary from rl.agents import DQNAgent from rl.memory impoort SequentialMemory def build_agent(model, actions): memory = SequentialMemory(limit=50000, window_length=1) dqn = DQNAgent(model=model, nb_actions=actions, nb_steps_warmup, target_model_update=1e-2) retturn dqn dqn = build_agent(model,actions) dqn.compile(Adam(lr=1e-3), metrics=['mae']) dqn.fit(env, nb_steps=50000, visualize=False, verbose=1 # Create the environment from gym import Env from gym.spaces import Discrete, Box import numpy as np import random action_movement_dict = { 0 : (-1,0), # North 1 : (0, 1), # East 2 : (1, 0), # South 3 : (0, -1) # West } class GridEnv(Env): def __init__(self, n=50): # TODO: Create grid? self.grid_len = n self.action_space = Discrete(4) self.observation_space = Discrete(n*n) self.state = (0,0) def step(self, action): dx, dy = action_movement_dict[action] # TODO: Check validity of state self.state[0] += dx self.state[0] += dy if self.state == (self.grid_len - 1, self.grid_len - 1): done = True else: done = False return self.state, reward, done, info def render(self): pass def reset(self): pass