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gridworld.py
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gridworld.py
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#!/usr/bin/env python
#MIT License
#Copyright (c) 2017 Massimiliano Patacchiola
# https://mpatacchiola.github.io/blog/
#
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is
#furnished to do so, subject to the following conditions:
#
#The above copyright notice and this permission notice shall be included in all
#copies or substantial portions of the Software.
#
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#SOFTWARE.
#Class for creating a gridworld of arbitrary size and with arbitrary obstacles.
#Each state of the gridworld should have a reward. The transition matrix is defined
#as the probability of executing an action given a command to the robot.
import numpy as np
class GridWorld:
def __init__(self, tot_row, tot_col):
self.action_space_size = 4
self.world_row = tot_row
self.world_col = tot_col
#The world is a matrix of size row x col x 2
#The first layer contains the obstacles
#The second layer contains the rewards
#self.world_matrix = np.zeros((tot_row, tot_col, 2))
self.transition_matrix = np.ones((self.action_space_size, self.action_space_size))/ self.action_space_size
#self.transition_array = np.ones(self.action_space_size) / self.action_space_size
self.reward_matrix = np.zeros((tot_row, tot_col))
self.state_matrix = np.zeros((tot_row, tot_col))
self.position = [np.random.randint(tot_row), np.random.randint(tot_col)]
#def setTransitionArray(self, transition_array):
#if(transition_array.shape != self.transition_array):
#raise ValueError('The shape of the two matrices must be the same.')
#self.transition_array = transition_array
def setTransitionMatrix(self, transition_matrix):
'''Set the reward matrix.
The transition matrix here is intended as a matrix which has a line
for each action and the element of the row are the probabilities to
executes each action when a command is given. For example:
[[0.55, 0.25, 0.10, 0.10]
[0.25, 0.25, 0.25, 0.25]
[0.30, 0.20, 0.40, 0.10]
[0.10, 0.20, 0.10, 0.60]]
This matrix defines the transition rules for all the 4 possible actions.
The first row corresponds to the probabilities of executing each one of
the 4 actions when the policy orders to the robot to go UP. In this case
the transition model says that with a probability of 0.55 the robot will
go UP, with a probaiblity of 0.25 RIGHT, 0.10 DOWN and 0.10 LEFT.
'''
if(transition_matrix.shape != self.transition_matrix.shape):
raise ValueError('The shape of the two matrices must be the same.')
self.transition_matrix = transition_matrix
def setRewardMatrix(self, reward_matrix):
'''Set the reward matrix.
'''
if(reward_matrix.shape != self.reward_matrix.shape):
raise ValueError('The shape of the matrix does not match with the shape of the world.')
self.reward_matrix = reward_matrix
def setStateMatrix(self, state_matrix):
'''Set the obstacles in the world.
The input to the function is a matrix with the
same size of the world
-1 for states which are not walkable.
+1 for terminal states
0 for all the walkable states (non terminal)
The following matrix represents the 4x3 world
used in the series "dissecting reinforcement learning"
[[0, 0, 0, +1]
[0, -1, 0, +1]
[0, 0, 0, 0]]
'''
if(state_matrix.shape != self.state_matrix.shape):
raise ValueError('The shape of the matrix does not match with the shape of the world.')
self.state_matrix = state_matrix
def setPosition(self, index_row=None, index_col=None):
''' Set the position of the robot in a specific state.
'''
if(index_row is None or index_col is None): self.position = [np.random.randint(tot_row), np.random.randint(tot_col)]
else: self.position = [index_row, index_col]
def render(self):
''' Print the current world in the terminal.
O represents the robot position
- respresent empty states.
# represents obstacles
* represents terminal states
'''
graph = ""
for row in range(self.world_row):
row_string = ""
for col in range(self.world_col):
if(self.position == [row, col]): row_string += u" \u25CB " # u" \u25CC "
else:
if(self.state_matrix[row, col] == 0): row_string += ' - '
elif(self.state_matrix[row, col] == -1): row_string += ' # '
elif(self.state_matrix[row, col] == +1): row_string += ' * '
row_string += '\n'
graph += row_string
print(graph)
def reset(self, exploring_starts=False):
''' Set the position of the robot in the bottom left corner.
It returns the first observation
'''
if exploring_starts:
while(True):
row = np.random.randint(0, self.world_row)
col = np.random.randint(0, self.world_col)
if(self.state_matrix[row, col] == 0): break
self.position = [row, col]
else:
self.position = [self.world_row-1, 0]
#reward = self.reward_matrix[self.position[0], self.position[1]]
return self.position
def step(self, action):
''' One step in the world.
[observation, reward, done = env.step(action)]
The robot moves one step in the world based on the action given.
The action can be 0=UP, 1=RIGHT, 2=DOWN, 3=LEFT
@return observation the position of the robot after the step
@return reward the reward associated with the next state
@return done True if the state is terminal
'''
if(action >= self.action_space_size):
raise ValueError('The action is not included in the action space.')
#Based on the current action and the probability derived
#from the trasition model it chooses a new actio to perform
action = np.random.choice(4, 1, p=self.transition_matrix[int(action),:])
#action = self.transition_model(action)
#Generating a new position based on the current position and action
if(action == 0): new_position = [self.position[0]-1, self.position[1]] #UP
elif(action == 1): new_position = [self.position[0], self.position[1]+1] #RIGHT
elif(action == 2): new_position = [self.position[0]+1, self.position[1]] #DOWN
elif(action == 3): new_position = [self.position[0], self.position[1]-1] #LEFT
else: raise ValueError('The action is not included in the action space.')
#Check if the new position is a valid position
#print(self.state_matrix)
if (new_position[0]>=0 and new_position[0]<self.world_row):
if(new_position[1]>=0 and new_position[1]<self.world_col):
if(self.state_matrix[new_position[0], new_position[1]] != -1):
self.position = new_position
reward = self.reward_matrix[self.position[0], self.position[1]]
#Done is True if the state is a terminal state
done = bool(self.state_matrix[self.position[0], self.position[1]])
return self.position, reward, done