repo_description: Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
repo_topics: actor-critic deep-reinforcement-learning dissecting-reinforcement-learning drone-landing genetic-algorithm inverted-pendulum markov-chain mountain-car multi-armed-bandit neural-networks q-learning reinforcement-learning sarsa temporal-differencing-learning
code: #!/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
  #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.
  
  #In this example I will use the class gridworld to generate a 3x4 world
  #in which the cleaning robot will move. Using the Monte Carlo method I
  #will estimate the utility values of each state.
  
  import numpy as np
  from gridworld import GridWorld
  
  def get_return(state_list, gamma):
      counter = 0
      return_value = 0
      for visit in state_list:
          reward = visit[1]
          return_value += reward * np.power(gamma, counter)
          counter += 1
      return return_value
  
  
  def main():
  
      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
      print("State Matrix:")
      print(state_matrix)
  
      #Define the reward matrix
      reward_matrix = np.full((3,4), -0.04)
      reward_matrix[0, 3] = 1
      reward_matrix[1, 3] = -1
      print("Reward Matrix:")
      print(reward_matrix)
  
      #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
      #This is the optimal policy for world with reward=-0.04
      policy_matrix = np.array([[1,      1,  1,  -1],
                                [0, np.NaN,  0,  -1],
                                [0,      3,  3,   3]])
      print("Policy Matrix:")
      print(policy_matrix)
  
      env.setStateMatrix(state_matrix)
      env.setRewardMatrix(reward_matrix)
      env.setTransitionMatrix(transition_matrix)
  
      utility_matrix = np.zeros((3,4))
      #init with 1.0e-10 to avoid division by zero
      running_mean_matrix = np.full((3,4), 1.0e-10) 
      gamma = 0.999
      tot_epoch = 50000
      print_epoch = 1000
  
      for epoch in range(tot_epoch):
          #Starting a new episode
          episode_list = list()
          #Reset and return the first observation and reward
          observation = env.reset(exploring_starts=False)
          for _ in range(1000):
              #Take the action from the action matrix
              action = policy_matrix[observation[0], observation[1]]
              #Move one step in the environment and get obs and reward
              observation, reward, done = env.step(action)
              #Append the visit in the episode list
              episode_list.append((observation, reward))
              if done: break
          #The episode is finished, now estimating the utilities
          counter = 0
          #Checkup to identify if it is the first visit to a state
          checkup_matrix = np.zeros((3,4))
          #This cycle is the implementation of First-Visit MC.
          #For each state stored in the episode list check if it
          #is the rist visit and then estimate the return.
          for visit in episode_list:
              observation = visit[0]
              row = observation[0]
              col = observation[1]
              reward = visit[1]
              if(checkup_matrix[row, col] == 0):
                  return_value = get_return(episode_list[counter:], gamma)
                  running_mean_matrix[row, col] += 1
                  utility_matrix[row, col] += return_value
                  checkup_matrix[row, col] = 1
              counter += 1
          if(epoch % print_epoch == 0):
              print("")
              print("Utility matrix after " + str(epoch+1) + " iterations:") 
              print(utility_matrix / running_mean_matrix)
      #Time to check the utility matrix obtained
      print("Utility matrix after " + str(tot_epoch) + " iterations:")
      print(utility_matrix / running_mean_matrix)
  
  
  
  if __name__ == "__main__":
      main()
  