OpenAI Gym's Taxi-v3 Task

Q-Learning solution for Taxi Problem from OpenAI Gym

We are using OpenAI Gym's Taxi-v3 environment to design an algorithm to teach a taxi agent to navigate a small gridworld.
The goal is to adapt all that you've learned in the previous lessons to solve a new environment!
States: There are 500 possible states, corresponding to 25 possible grid locations, 5 locations for the passenger, and 4 destinations.
Actions : There are 6 possible actions, corresponding to moving North, East, South, or West, picking up the passenger, and dropping off the passenger.

The workspace contains three files:

1. agent.py: Developed reinforcement learning agent here.
2. monitor.py: The interact function tests how well your agent learns from interaction with the environment.
3. main.py: Run this file in the terminal to check the performance of your agent.

Solving : OpenAI Gym defines "solving" this task as getting average return of 9.7 over 100 consecutive trials.

Reference

https://arxiv.org/pdf/cs/9905014.pdf