RL_Agents: A collection of RL Agents / Algorithms implemented on 3 simple Environments

Copyright for portions of these lab files are held by Denny Britz as part of https:/dennybritz/reinforcement-learning and by Microsoft as part of their online edX course "Reinforcement Learning Explained - DAT257x" https://notebooks.azure.com/Microsoft-Learning/projects/DAT257x. Copyright (c) 2016 Denny Britz Copyright (c) 2017-present Microsoft

The project should be organized in the following folder structure:

• RL_Agents (main project folder)
  • lib
    • plotting.py
    • simulation.py
    • envs
      • simple_rooms.py
      • windy_gridworld.py
      • cliff_walking.py
  • experiments.py
  • sarsa_agent.py
  • Q_learning_agent.py
  • README.md

The project requires the following libraries:

1. Gym
2. Numpy
3. Pandas
4. Matplotlib

Running

The main file to run is experiments.py. It is located in RL_Agents ( main project folder).

Required arguments:

'--interactive': Set it to 'True' to render and see the agent while it learns and acts in the environment (Not required. Default value of False)

'--env': Environment to run the agent on (required). Three environments are available:

1. Simple Gridworld
2. Windy Gridworld
3. Cliff Walking

legal values for the --env argument:

• For the Cliff Walking Env : "cliffwalking" "cliffwalkingenv" "cliff" "cliffs"
• For the Windy Gridworld Env : "windygridworld" "windygridworldenv" "windygrid" "windy"
• For the Simple Gridworld Env : "simplemaze" "simplegrid" "simplegridworld" "simplegridworldenv" "simpleroomsenv" "simpleroom" "maze" "grid"

'--agent':Type of Agent Algorithm to execute (required). Two algorithms are available:

1. SARSA
2. Q-Learning legal values for the --agent argument:

• For the SARSA Agent >> any string that starts with an "S" (or "s")
• For the Q Learning Agent >> any string that stars with "Q" (or "q")

'--iter': Number of iterations to run the algorithm for (required integer)

'--epsilon': Value of exploration probability Epsilon (Not required float. Default value of 0.9)

'--alpha' : Value for the learning rate Alpha (Not required float. Default value of 0.5)

'--decay' : How often / the number of time steps after which Epsilon is decayed (Not required integer. Default value of 50)

Example Usage

Interactive mode of the Sarsa agent on the Simple Girdworld environment for 20 iterations

 python experiments.py --env=grid --interactive=True --agent=s --iter=20 --epsilon=0.9 --alpha=0.5 --decay=30

Non-Interactive mode of the Q Learning agent on the Cliff Walking environment for 340 iterations

 python experiments.py --env=cliff --agent=q --iter=340 --epsilon=0.7 --alpha=0.5 --decay=30

Non-Interactive mode of the SARSA agent on the Cliff Walking environment for 200 iterations

 python experiments.py --env=wind --agent=sarsa --iter=200 --epsilon=1.0 --alpha=0.5 --decay=30

Results in the Project Decription pdf can be replicated using the following commands:

python experiments.py --env=CliffWalking --agent=sarsa --iter=1000 --epsilon=0.9 --alpha=0.5 --decay=30
python experiments.py --env=CliffWalking --agent=q --iter=1000 --epsilon=0.7 --alpha=0.5 --decay=30

python experiments.py --env=Windy Gridworld --agent=q --iter=1000 --epsilon=0.9 --alpha=0.5 --decay=30
python experiments.py --env=Windy Gridworld --agent=sarsa --iter=2000 --epsilon=0.9 --alpha=0.5 --decay=30