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mazeDiscrete.py
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mazeDiscrete.py
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# authors: anonymized
from __future__ import division, print_function
import numpy as np
from scipy.spatial.distance import cdist, squareform
class Maze():
# The walls are defined using the coordinates of two points
def __init__(self, x_max, y_max, walls, x_end, y_end, RBF_shape='small', env_type=0):
self.x_max = x_max
self.y_max = y_max
self.walls = walls
self.x_end = x_end
self.y_end = y_end
self.env_type = env_type
self.good_rare_pairs = [(int(x_max*(y_max-1)), 1), (int(2 + x_max*(y_max-1)), 3), (int(1 + x_max*(y_max-2)), 0)] if env_type == 1 else None
self.nb_states = int(x_max * y_max)
self.nb_actions = 4
self.set_transition_function()
def set_transition_function(self):
self.transition_function = np.zeros((self.nb_states, 4, self.nb_states))
for x in range(self.x_max):
for y in range(self.y_max):
s = x + y*self.y_max
if s < 24:
if self.has_hit(x, x, y, y+1):
s2 = s
else:
s2 = x + (y+1) * self.y_max
self.transition_function[s,0,s2] += 0.75
self.transition_function[s,1,s2] += 0.10
self.transition_function[s,2,s2] += 0.05
self.transition_function[s,3,s2] += 0.10
if self.has_hit(x, x, y, y-1):
s2 = s
else:
s2 = x + (y-1) * self.y_max
self.transition_function[s,0,s2] += 0.05
self.transition_function[s,1,s2] += 0.10
self.transition_function[s,2,s2] += 0.75
self.transition_function[s,3,s2] += 0.10
if self.has_hit(x, x+1, y, y):
s2 = s
else:
s2 = x + 1 + y * self.y_max
self.transition_function[s,0,s2] += 0.10
self.transition_function[s,1,s2] += 0.75
self.transition_function[s,2,s2] += 0.10
self.transition_function[s,3,s2] += 0.05
if self.has_hit(x, x-1, y, y):
s2 = s
else:
s2 = x - 1 + y * self.y_max
self.transition_function[s,0,s2] += 0.10
self.transition_function[s,1,s2] += 0.05
self.transition_function[s,2,s2] += 0.10
self.transition_function[s,3,s2] += 0.75
# Check if the agent has hit the wall
def has_hit(self, x, x_new, y, y_new):
if x_new < 0 or x_new >= self.x_max or y_new < 0 or y_new >= self.y_max:
return True
for wall in self.walls:
if self.intersect(x, y, x_new, y_new, wall):
return True
return False
# Reward matrix
def compute_reward(self):
nb_states = int(self.x_max * self.y_max)
if self.env_type == 1:
nb_states = nb_states * 2
R = np.zeros((nb_states, nb_states))
for s in range(nb_states):
R[s, nb_states - 1] = 100
if self.env_type == 1:
R[s, int(nb_states/2) - 1] = 100
R[s, int(1 + self.x_max*(self.y_max-1))] = 100 # add a second closer goal
R[s, int(1 + self.x_max*(self.y_max-1) + self.x_max*self.y_max)] = 0
R[s, s] = -10
return R
# Get the current feature
def get_features(self, x, y):
return np.array([x, y])
# Check if a wall was hit
def intersect(self, x, y, x_new, y_new, coord):
if x_new != x or y_new != y:
if x_new == x:
x = x + 0.01
else:
y = y_new + 0.1
x_inter = ((x * y_new - y * x_new) * (coord[0][0] - coord[1][0]) - (x - x_new) * (
coord[0][0] * coord[1][1] - coord[0][1] * coord[1][0])) / (
(x - x_new) * (coord[0][1] - coord[1][1]) - (y - y_new) * (coord[0][0] - coord[1][0]))
y_inter = ((x * y_new - y * x_new) * (coord[0][1] - coord[1][1]) - (y - y_new) * (
coord[0][0] * coord[1][1] - coord[0][1] * coord[1][0])) / (
(x - x_new) * (coord[0][1] - coord[1][1]) - (y - y_new) * (coord[0][0] - coord[1][0]))
if (coord[0][0] == coord[1][0]):
if (x_inter <= max(x, x_new)) and (x_inter >= min(x, x_new)):
if ((y_inter <= max(coord[0][1], coord[1][1]))) and (
(y_inter >= (min(coord[0][1], coord[1][1])))) and (y_inter <= max(y, y_new)) and (
(y_inter >= min(y, y_new))):
return True
if coord[0][1] == coord[1][1]:
if (x_inter <= max(coord[0][0], coord[1][0])) and (x_inter >= min(coord[0][0], coord[1][0])) and (
x_inter <= max(x, x_new)) and (x_inter >= min(x, x_new)):
if (y_inter <= max(y, y_new)) and (y_inter >= min(y, y_new)):
return True
if (x_inter <= max(coord[0][0], coord[1][0])) and (x_inter >= min(coord[0][0], coord[1][0])) and (
x_inter <= max(x, x_new)) and (x_inter >= min(x, x_new)):
if ((y_inter <= max(coord[0][1], coord[1][1]))) and ((y_inter >= (min(coord[0][1], coord[1][1])))) and (
y_inter <= max(y, y_new)) and ((y_inter >= min(y, y_new))):
return True
return False
else:
return False
# Return the coordinates of the intersection with the wall
def get_intersect(self, x, x_new, y, y_new):
walls = self.walls + [[[0, 0], [self.x_max, 0]], [[self.x_max, 0], [self.x_max, self.y_max]],
[[0, 0], [0, self.y_max]], [[0, self.y_max], [self.x_max, self.y_max]]]
if x_new != x or y_new != y:
if x_new < 0:
if y_new > 0 and y_new < self.y_max:
return (0.01, y_new)
elif y_new > self.y_max:
return (0.01, self.y_max - 0.02)
else:
return (0.01, 0.01)
if y_new < 0:
if x_new > 0 and x_new < self.x_max:
return (x_new, 0.01)
elif x_new > self.x_max:
return (self.x_max - 0.02, 0.01)
else:
return (0.01, 0.01)
if y_new > self.y_max:
if x_new < self.x_max:
return (x_new, self.y_max - 0.02)
else:
return (self.x_max - 0.02, self.y_max - 0.02)
if x_new > self.x_max:
return (self.x_max - 0.02, y_new)
for coord in walls:
x_inter = ((x * y_new - y * x_new) * (coord[0][0] - coord[1][0]) - (x - x_new) * (
coord[0][0] * coord[1][1] - coord[0][1] * coord[1][0])) / (
(x - x_new) * (coord[0][1] - coord[1][1]) - (y - y_new) * (
coord[0][0] - coord[1][0]))
y_inter = ((x * y_new - y * x_new) * (coord[0][1] - coord[1][1]) - (y - y_new) * (
coord[0][0] * coord[1][1] - coord[0][1] * coord[1][0])) / (
(x - x_new) * (coord[0][1] - coord[1][1]) - (y - y_new) * (
coord[0][0] - coord[1][0]))
if (coord[0][0] == coord[1][0]):
if (x_inter <= max(x, x_new)) and (x_inter >= min(x, x_new)):
if ((y_inter <= max(coord[0][1], coord[1][1]))) and (
(y_inter >= (min(coord[0][1], coord[1][1])))) and (y_inter <= max(y, y_new)) and (
(y_inter >= min(y, y_new))):
return (x_inter, y_inter)
if coord[0][1] == coord[1][1]:
if (x_inter <= max(coord[0][0], coord[1][0])) and (x_inter >= min(coord[0][0], coord[1][0])) and (
x_inter <= max(x, x_new)) and (x_inter >= min(x, x_new)):
if (y_inter <= max(y, y_new)) and (y_inter >= min(y, y_new)):
return (x_inter, y_inter)
if (x_inter <= max(coord[0][0], coord[1][0])) and (x_inter >= min(coord[0][0], coord[1][0])) and (
x_inter <= max(x, x_new)) and (x_inter >= min(x, x_new)):
if ((y_inter <= max(coord[0][1], coord[1][1]))) and (
(y_inter >= (min(coord[0][1], coord[1][1])))) and (y_inter <= max(y, y_new)) and (
(y_inter >= min(y, y_new))):
return (x_inter, y_inter)
else:
return (x_new, y_new)
# Check if the maze is complete
def isComplete(self, x, y):
return (x == self.x_end) and (y == self.y_end)
class Player():
def __init__(self, x_init, y_init, maze):
self.x_init = x_init
self.y_init = y_init
self.x = x_init
self.y = y_init
self.has_hit = False
self.reward = 0
self.maze = maze
self.game_over = False
self.env_type = maze.env_type
self.candy_eaten = np.zeros(2)
# Define a move for the agent
def move(self, action):
new_x, new_y = action(self.x, self.y)
self.has_hit = self.maze.has_hit(self.x, new_x, self.y, new_y)
if self.has_hit:
self.x = self.x
self.y = self.y
else:
self.x, self.y = new_x, new_y
# Update the state for a given action
def update_state(self, action):
self.move(action)
if self.has_hit:
self.reward = -10
elif self.maze.isComplete(self.x, self.y):
# This value can be modified
self.reward = 100
self.game_over = True
elif self.env_type == 1 and (self.x, self.y) == (1, self.maze.y_max-1):
if not self.candy_eaten[0]:
self.reward = 100
self.candy_eaten[0] = 1
else:
self.reward = 0
self.candy_eaten[1] = 1
else:
self.reward = 0
# Return the state of the agent
def get_current_state(self):
itd = self.has_hit
st = (self.x, self.y, itd, self.reward, self.game_over, self.candy_eaten)
self.has_hit = False
return st
class Action():
def __init__(self, action="N"):
self.action_dict = {0: self.north,
1: self.east,
2: self.south,
3: self.west}
self.action = action
def __call__(self, x, y):
return self.action_dict[self.action % 4](x, y)
def east(self, x, y):
choice = np.random.rand()
if choice < 0.75:
return (x + 1, y)
elif choice < 0.85:
return (x, y + 1)
elif choice < 0.95:
return (x, y - 1)
else:
return (x - 1, y)
def west(self, x, y):
choice = np.random.rand()
if choice < 0.75:
return (x - 1, y)
elif choice < 0.85:
return (x, y + 1)
elif choice < 0.95:
return (x, y - 1)
else:
return (x + 1, y)
def north(self, x, y):
choice = np.random.rand()
if choice < 0.75:
return (x, y + 1)
elif choice < 0.85:
return (x + 1, y)
elif choice < 0.95:
return (x - 1, y)
else:
return (x, y - 1)
def south(self, x, y):
choice = np.random.rand()
if choice < 0.75:
return (x, y - 1)
elif choice < 0.85:
return (x + 1, y)
elif choice < 0.95:
return (x - 1, y)
else:
return (x, y + 1)