gridworld_test.py

#!/usr/bin/env python

#MIT License
#Copyright (c) 2017 Massimiliano Patacchiola
#
#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
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#copies of the Software, and to permit persons to whom the Software is
#furnished to do so, subject to the following conditions:
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#The above copyright notice and this permission notice shall be included in all
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#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
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#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#SOFTWARE.

#In this example I will use the class gridworld to generate a 3x4 world
#in which the cleaning robot will move. In this example the robot follows
#a policy which is optimal, reaching the terminal state (+1) with high probability.

import numpy as np
from gridworld import GridWorld

env = GridWorld(3, 4)

#Define the state matrix
state_matrix = np.zeros((3,4))
state_matrix[0, 3] = 1
state_matrix[1, 3] = 1
state_matrix[1, 1] = -1

#Define the reward matrix
reward_matrix = np.full((3,4), -0.04)
reward_matrix[0, 3] = 1
reward_matrix[1, 3] = -1

#Define the transition matrix
transition_matrix = np.array([[0.8, 0.1, 0.0, 0.1],
                              [0.1, 0.8, 0.1, 0.0],
                              [0.0, 0.1, 0.8, 0.1],
                              [0.1, 0.0, 0.1, 0.8]])

#Define the policy matrix
policy_matrix = np.array([[1,  1,  1,  0],
                          [0, -1,  0,  0],
                          [0,  3,  3,  3]])


env.setStateMatrix(state_matrix)
env.setRewardMatrix(reward_matrix)
env.setTransitionMatrix(transition_matrix)

#Reset the environment
observation = env.reset()
env.render()

for _ in range(1000):
    action = policy_matrix[observation[0], observation[1]]
    observation, reward, done = env.step(action)
    print("")
    print("ACTION: " + str(action))
    print("REWARD: " + str(reward))
    print("DONE: " + str(done))
    env.render()
    if done: break