Btt1996/rubikCube-reinforcementLearning

rubikCube-reinforcementLearning

Rubik's Cube Solver using Reinforcement Learning

This repository contains code for training and evaluating a Rubik's Cube solver using reinforcement learning.

Files

• agent.py - defines the reinforcement learning agent
• environment.py - defines the Rubik's Cube environment
• main.py - main script to train and evaluate the Rubik's Cube solver
• neural.py - defines the neural network architecture for the agent
• optimiser.py - defines the optimizer used to train the agent
• policy.py - defines the policy used by the agent to select actions
• trainer.py - defines the training process for the agent
• evaluator.py - defines the evaluation process for the agent
• trainer_options.py - defines options and hyperparameters for the training process
• evaluator_options.py - defines options and hyperparameters for the evaluation process
• utils.py - utility functions used throughout the project

Database

A database can be useful for storing and retrieving information about the training process and the agent's performance. For example, you could use a database to store:

• The agent's current state and weights
• The agent's performance metrics during training (e.g., average reward, success rate, etc.)
• The configuration settings for the agent and environment

This information can then be used to resume training from a previous checkpoint or to compare the performance of different agents or configurations.

One popular database for machine learning projects is SQLite, which is a lightweight, file-based database that can be easily integrated into Python projects. Other options include MySQL, PostgreSQL, and MongoDB.

If you decide to add a database to this repository, be sure to include any necessary installation and setup instructions in the README.md file.

Contributing

Contributions to this project are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue on GitHub.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.