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Implement reinforcement learning algorithm to find policies for frozenlake environment.

Reinforcement learning algorithms implemented:

1. Model based tabular algorithms:
   • Policy iteration
   • Value iteration
   • Policy improvement
2. Model free tabular algorithms:
   • SARSA control
   • Q-learning control
3. Model free non-tabular algorithms:
   • SARSA control with Linear function approximation
   • Q-learning with Linear function approximation

Unzip the folder, cd onto the project directory and run pip install -r requirements.txt from terminal to install the required dependencies.

• python version should be 3.7 or above
• numpy version should be 1.19.2 or above
• matplotlib version should be 3.3.2 or above

• Run python main_implementation.py file to run the reinforcement learning algorithms on the environments.
• Run python run_env.py file to manually run the small frozenlake environment.

• small_lake_implementation() method in main_implementation.py displays the policy and values for the small frozenlake, comment out line 135 if you want to execute only for small frozenlake.
• big_lake_implementation() method in main_implementation.py displays the policy and values for the big frozenlake, comment out line 136 if you want to execute only for big frozenlake.

• run_env.py runs the small frozenlake environment by default.
• To manually run the big frozenlake environment, comment line 85 and uncomment line 86

• Data collector is used to visualize the convergence of the algorithms.
• It computes mean squared error for value when compared to optimal value.
• It computes the classification error for policy when compared to optimal policy.
• It has been commented out as it affects performance. Follow the below instructions to use data collector.

Add below to the algorithm to store the error for visualization.
    
    DataCollectorSingleton.instance().calculate_error("Algorithm name", policy, value)
   
Add the below to the main_implementation.py to visualize the errors

before running algorithms:

    DataCollectorSingleton.instance().set_optimal_policy_value("env name", "env optimal policy value path <.npy file>")
    
after running algorithms:

    plot_errors(*DataCollectorSingleton.instance().get_errors()) 

• Karthik Bhamidipati
• Omer Faruk Yildirim
• Sudipta Mondal