Solving the Taxi Problem Using OpenAI Gym and Reinforcement Learning
In this post, we will explore the Taxi-v3 environment from OpenAI Gym and use a simple Q-learning algorithm to solve it. The Taxi problem is an excellent introduction to reinforcement learning, where an agent must navigate a grid to transport passengers to their destinations.
We’ll go over:
- What the Taxi-v3 problem is,
- The code implementation using Q-learning,
- Visualizing the trained taxi agent in action.
The full code associated to this post can be found at this GitHub page.
Let’s get started!
What is the Taxi Problem?
The Taxi-v3 problem is a classic reinforcement learning problem where a taxi must transport passengers to their destinations on a 5x5 grid. The environment has several fixed locations (denoted by the letters R, Y, G, and B for Red, Yellow, Green, and Blue). These letters represent the possible pickup and dropoff locations for passengers.
The taxi starts in a random position, and at each episode, the passenger’s location and destination are randomly assigned to one of these four spots. The agent (taxi) must figure out the best sequence of moves to:
- Navigate to the…
