LLM backed Chess Reinforcement Learning Documentation

---

Checkers Reinforcement Learning Model

This documentation provides an overview of the training process and architecture of a reinforcement learning model designed to play the game of checkers. It incorporates a hybrid Deep Q-Learning approach, combined with custom self-play and evaluation mechanisms, with a core integration of a Language Model (LLM) to limit the action space effectively.

Key Features

• Reinforcement Learning (RL): Utilizes temporal difference learning with a neural network to approximate Q-values for state-action pairs.

• Self-Play: The model trains against itself to iteratively improve its gameplay.

• Custom Opponents: Supports training against various opponents, including random moves, minimax strategies, and itself.

• LLM-Driven Action Space Limitation: A Language Model (LLM) acts as an integral part of the architecture, dynamically reducing the action space to the most promising options.

• Exploration-Exploitation Balance: Implements a dynamic exploration parameter to balance random exploration and policy exploitation.

• Performance Metrics: Tracks win rates over generations for performance evaluation.

Introduction

• Introduction
  • Overview
  • Motivation
  • Goals
  • Key Features
  • Next Steps

Implementation

• Implementation Details
  • Agent Architecture
  • Training Process
  • LLM Integration
  • Generations
  • Next Steps

LLM Integration

• LLM Integration for Checkers Training with Reinforcement Learning
  • LLM Used
  • LLM Integration Steps
  • Results

Interface

• Interactive Interface
  • Game Interface
  • Overview
  • Features
  • Usage
  • Enhancements
  • Dependencies
  • Important Note
• Next Steps - Applying project knowledge in Chess RLLM