opendilab/LightZero

Health v2 · maintenance only

Adoption not part of the score

1636 stars · 197 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LightZero is a lightweight, open-source Python toolkit that unifies Monte Carlo Tree Search (MCTS) with deep reinforcement learning, implementing algorithms like AlphaZero, MuZero, EfficientZero, Gumbel MuZero, and Stochastic MuZero. It serves as both an algorithm library and a benchmark for evaluating MCTS-based methods across board games, Atari, and continuous control tasks.

Use cases

• train an AlphaZero agent to play Gomoku or TicTacToe from self-play
• benchmark MuZero-style algorithms on Atari environments
• apply MCTS-based RL to continuous control problems
• compare EfficientZero, Sampled MuZero, and Gumbel MuZero implementations
• learn how Monte Carlo Tree Search combined with deep RL works
• run a unified benchmark for sequential decision-making algorithms

When to choose

• you need ready-made, well-tested implementations of MCTS+RL algorithms like MuZero or EfficientZero
• you want a unified benchmark to compare tree-search RL methods across game and control environments
• you are doing research on MCTS-based decision-making and want a PyTorch-based, customizable toolkit

When to avoid

• you need general-purpose RL algorithms without tree search, such as PPO or SAC alone
• you want a production game AI engine rather than a research toolkit
• your project requires non-Python or extremely low-latency inference outside PyTorch

Facets

library · maturity active

reinforcement-learning machine-learning simulation benchmarking sdk reinforcement-learning machine-learning python windows mcts muzero alphazero efficientzero self-play board-games pytorch decision-making neurips game-development algorithms research gpu linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="opendilab/LightZero")