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Leela Zero is a deep learning Go engine and reinforcement learning system that implements the AlphaGo Zero approach. It utilizes deep residual convolutional networks and Monte Carlo Tree Search to determine optimal moves and analyze the game of Go.
The main features of leela-zero/leela-zero are: Deep Learning Game AIs, Deep Reinforcement Learning Implementations, Monte Carlo Tree Search, Neural Network Training Frameworks, Move Selection Networks, Reinforcement Learning, Residual Networks, Self-Play Training Pipelines.
Projects with overlapping indexed features include: leelachesszero/lc0 — Leela Chess Zero is a deep learning game AI and neural network chess engine that uses search algorithms to determine… suragnair/alpha-zero-general — This project is a reinforcement learning framework and game AI engine designed for training adversarial agents in… keras-rl/keras-rl — keras-rl is a reinforcement learning library that enables the training of neural agents using Keras. It serves as a… tensorflow/minigo — Minigo is a TensorFlow-based reinforcement learning engine designed to master the game of Go. It functions as a… tensorlayer/tensorlayer — TensorLayer is a backend-agnostic tensor library and deep learning framework designed for building neural network… tensorpack/tensorpack — Tensorpack is a high-level TensorFlow neural network framework and research library designed for building and training…
Leela Chess Zero is a deep learning game AI and neural network chess engine that uses search algorithms to determine optimal moves and evaluate game states. It functions as a UCI chess engine, implementing the Universal Chess Interface standard for compatibility with various graphical user interfaces. The system acts as a hardware-accelerated move calculator, leveraging GPU and CPU backends to accelerate neural network inference. It supports the generation and submission of self-play games to training clients to improve the strength of its neural network models. The engine provides capabilit
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
keras-rl is a reinforcement learning library that enables the training of neural agents using Keras. It serves as a framework for implementing deep reinforcement learning agents that interact with simulated environments to discover optimal behaviors and maximize cumulative rewards. The library provides a system for configuring, training, and managing neural network agents. It handles the interaction loop between agents and environments, allowing models to learn through direct experience and gradient-based optimization. The framework includes capabilities for model weight management, allowing
Minigo is a TensorFlow-based reinforcement learning engine designed to master the game of Go. It functions as a comprehensive system for training neural networks to predict board policies and game outcomes, utilizing a model trainer to generate self-play data and optimize weights. The project is distinguished by its ability to perform large-scale game simulations using Kubernetes to distribute worker nodes across CPU, GPU, and TPU hardware. It employs a Monte Carlo Tree Search implementation to identify optimal moves and supports specialized hardware acceleration, including inference on Edge