suragnair/alpha-zero-general
Alpha Zero General
This project is a reinforcement learning framework and game AI engine designed for training adversarial agents in two-player turn-based games. It implements a training loop that utilizes self-play and Monte Carlo Tree Search to produce neural networks capable of predicting board strength and move probabilities.
The system decouples the reinforcement learning engine from specific game rules through an abstract game logic interface, allowing for the definition of custom game rules, win conditions, and board representations. It supports integration with various deep learning frameworks to serve as policy and value functions and includes a dual-head neural network architecture to predict both expected outcomes and legal move distributions.
The framework covers a broad range of capabilities including game state encoding, model checkpoint management, and training metric tracking. It provides tools for evaluating agent performance through tournament simulations, human-agent interfaces, and a text-based protocol for connecting to external game engines.
Trained models can be exported to a JavaScript-compatible format for use in web-based deployment environments.
Features
- Deep Learning Game AIs - A framework for training AI agents to master complex adversarial games through self-play and deep neural networks.
- Reinforcement Learning Training Loops - Implements a complete self-play reinforcement learning loop for training adversarial agents in turn-based games.
- Adversarial Game Engines - A flexible implementation for developing and testing neural network agents for turn-based adversarial games.
- Training Loops - Implements a training loop that produces policy and value networks to predict board strength.
- Game State Predictors - Provides models that predict optimal next moves and final game outcomes from a given board state.
- Policy and Value Function Approximators - Implements neural network architectures that approximate both the board value and the action policy for game AI.
- Monte Carlo Tree Search - Integrates Monte Carlo Tree Search to simulate future game states and select the optimal move.
- Neural Network Training - Provides the process for iteratively updating neural network weights based on self-play simulation data.
- Reinforcement Learning Implementations - Provides a full implementation of reinforcement learning agents for turn-based adversarial games.
- Reinforcement Learning Research Frameworks - A comprehensive reinforcement learning research framework based on the AlphaZero architecture for adversarial AI.
- Self-Play Training Pipelines - Utilizes a pipeline that generates game data via AI self-play to iteratively improve agent strategies.
- Game Logic Interfaces - Provides an abstract interface to decouple game rules from the reinforcement learning engine.
- Deep Learning Policy Frameworks - Supports the integration of various deep learning frameworks to act as policy and value function approximators.
- Game Logic Decouplers - Provides an abstract interface to decouple reinforcement learning logic from specific game rules and mechanics.
- Game AI Performance Benchmarking - Evaluates AI agent performance through tournament simulations and win-rate tracking against humans or baselines.
- Game State Feature Encoding - Transforms raw game board states into numerical tensors and one-hot encoded feature planes for ML input.
- Game Episode Evaluators - Evaluates agent performance by simulating game episodes and tracking win rates in tournaments.
- Model Performance Evaluators - Quantifies model reliability by comparing win rates against baseline players.
- Policy Refinement - Refines action estimates and selects optimal moves by combining MCTS with neural network evaluations.
- Dual-Head Architectures - Implements a dual-head neural network to simultaneously predict move probabilities and game outcome values.
- Agent Match Simulations - Matches two AI agents or a human and an AI to evaluate performance and strategy.
- Extensible Game Logic - Allows users to integrate custom game rules and board representations by extending provided base classes.
- Game Rule Validation - Provides mechanisms to define and validate legal moves and win conditions for adversarial turn-based games.
- State Representations - Models turn-based environments with grid-based positioning and win conditions.
- Reinforcement Learning - AlphaZero methodology implementation.
- Unified Toolkits - Generic implementation of the AlphaZero algorithm for board games.
Star history
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