OpenAI-Gym-Taxi-v2

This small repo represents a re-inforcement solution to the Taxi problem in OpenAI Gym: https://github.com/openai/gym/wiki/Leaderboard#taxi-v2

Steps to Run

1. Clone the repo: git clone https://github.com/mostafaelhoushi/OpenAI-Gym-Taxi-v2

2. cd to the workspace directory: cd OpenAI-Gym-Taxi-v2/workspace

3. Run the main script: python main.py You may add any of the following arguments when calling the above command to specify the update method: SARSA, SARSA_MAX, EXPECTED_SARSA.

Source Code:

The repo contains three files in its workspace folder:

• agent.py: The code I develop the reinforcement learning agent is written here here. This is the only file that I have modified.
• monitor.py: The interact function tests how well the agent learns from interaction with the environment. This file has been provided by the creators of the Udacity Reinforcement Learning Nanodegree.
• main.py: The main file to run in the terminal to check the performance of the agent. This file has been provided by the creators of the Udacity Reinforcement Learning Nanodegree.

Results:

The average of running 100 episodes for Sarsa Max (a.k.a. Q-Learning) is 9.2926, Expected Sara is 9.2754.