Q-Learning with Cartpole Problem of OpenAI Gym

Author: Magi Chen chenmagi@gmail.com

This script implements the Q-Learning algorithm to solve the Cartpole problem from the OpenAI Gym environment. The goal is to teach an agent to balance a pole on a moving cart for as long as possible.

Environment:

• Action Space: The agent can take two actions - pushing the cart to the left or the right.
• Observation Space: The observations include the cart's position, velocity, pole angle, and pole angular velocity.

Key Components:

1. MappingConfig: A class that helps to map and discretize the continuous observation values to discrete states.

2. LearningRateControl: A class that controls the exploration and learning rates during training.

3. Q_Learning: The main class that encapsulates the Q-Learning algorithm. It includes methods to select actions, update Q-values, and run the Q-Learning process.

Usage:

1. Ensure you have the necessary dependencies installed: gym, numpy, matplotlib. You can install them using pip:

   pip install -r requirements.txt