def run_rl():
walls = [5, 6, 13]
height = 4
width = 5
m = build_maze(width, height, walls, hit=True)
q = policy_iteration_q(m, render=False)
pol = get_policy_from_q(q)
print("TD-learning")
temporal_difference(m, pol, render=True)
print("Q-learning")
q_learning(m, tau=6)
print("Sarsa")
sarsa(m, tau=6)
input("press enter")
def create_maze(width, height, ratio):
size = width * height
n_walls = round(ratio * size)
stop = False
m = None
# the loop below is used to check that the maze has a solution
# if one of the values after value iteration is null, then another maze should be produced
while not stop:
walls = random.sample(range(size), int(n_walls))
m = build_maze(width, height, walls)
v, _, _, _ = value_iteration_v(m, render=False)
if np.all(v):
stop = True
return m
def run_dyna_prog():
# walls = [7, 8, 9, 10, 21,27,30,31,32,33,45, 46, 47]
# height = 6
# width = 9
#
m = build_maze(width, height, walls) # maze-like MDP definition
# m = create_maze(10, 10, 0.2)
m.render()
# plot_convergence_vi_pi(m, False)
#
print("value iteration V")
cpt = Chrono()
q, _, nbIter, nbUd = value_iteration_v(m, render=0)
print(nbIter, nbUd)
print(len(q))
cpt.stop()
def run_rl():
walls = [5, 6, 13]
height = 4
width = 5
m = build_maze(width, height, walls, hit=True)
# m = create_maze(8, 8, 0.2)
print("1")
q,_,_,_ = policy_iteration_q(m, render=0)
print("1")
pol = get_policy_from_q(q)
# print("TD-learning")
# temporal_difference(m, pol, render=True)
# input("press enter")
# print("Q-learning")
# q_learning_eps(m, tau=6)
plot_ql_sarsa_para(m, 0.001, 6, 1000, 50, 0.5, False)
def run_dyna_prog():
# walls = [14, 15, 16, 31, 45, 46, 47]
# height = 6
# width = 9
walls = [5, 6, 13]
height = 4
width = 5
m = build_maze(width, height, walls) # maze-like MDP definition
print("value iteration V")
value_iteration_v(m, render=True)
print("value iteration Q")
value_iteration_q(m, render=True)
print("policy iteration Q")
policy_iteration_q(m, render=True)
print("policy iteration V")
policy_iteration_v(m, render=True)
input("press enter")
from maze import build_maze from room import Room maze = build_maze() maze.explore_maze()
from random import randint
from math import inf
from maze import build_maze, explore_maze, print_maze_paths, mow
from graph import dijkstras, a_star, bfs, quicksort
from colors import colors
#define grid size
grid_size_row = 15
grid_size_colmun = 35
#swag list
swags = ['*', '+', 'candy corn', 'werewolf', 'pumpkin', 'apple', 'banana', 'diamond', 'melon', 'watermelon', 'kiwi']
# building maze
grid, start_i, start_j, end_i, end_j = build_maze(grid_size_row, grid_size_colmun, swags)
# rendering maze
print( colors.CBLUE2 + " ## Maze ## " + colors.CEND )
print_maze_paths(grid, None)
print("\nGrid size row: {0}, column: {1}".format(grid_size_row, grid_size_colmun))
print("Start point: {0},{1}\nEnd point: {2},{3}".format(start_i, start_j, end_i, end_j))
# run dijkstras's algorithm to find a path to the end
paths_and_distances, count = dijkstras(grid, start_i, start_j)
print("\n" + colors.CBLUE2 + " ## Dijkstras ## " + colors.CEND)
print_maze_paths(grid, paths_and_distances[end_i][end_j][1])
# Debug only
#print("\ndistances for each cell:\n")
#for row in paths_and_distances: