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LightZero

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Updated on 2026.03.11 LightZero-v0.2.0

LightZero is a lightweight, efficient, and easy-to-understand open-source algorithm toolkit that combines Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (RL).

English | 简体中文(Simplified Chinese) | Documentation | LightZero Paper | UniZero Paper | ReZero Paper | 🔥ScaleZero Paper

News

• [2026.02] 🔥 The ScaleZero paper has been accepted as a conference paper at ICLR 2026: One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning.
• [2025.08] The ReZero paper has been accepted by the CoRL 2025 RemembeRL workshop.
• [2025.06] The UniZero paper has been accepted by Transactions on Machine Learning Research (TMLR 2025).
• [2023.09] The LightZero paper has been accepted as a Spotlight Presentation at the NeurIPS 2023 Datasets and Benchmarks Track.
• [2023.04] LightZero v0.0.1 was officially released.

🔍 Background

The integration of Monte Carlo Tree Search and Deep Reinforcement Learning, exemplified by AlphaZero and MuZero, has achieved unprecedented performance levels in various games, including Go and Atari. This advanced methodology has also made significant strides in scientific domains like protein structure prediction and the search for matrix multiplication algorithms. The following is an overview of the historical evolution of the Monte Carlo Tree Search algorithm series:

🎨 Overview

LightZero is an open-source algorithm toolkit that combines Monte Carlo Tree Search (MCTS) and Reinforcement Learning (RL) for PyTorch. It supports a range of MCTS-based RL algorithms and applications, offering several key advantages:

• Lightweight.
• Efficient.
• Easy-to-understand.

For further details, please refer to Features, Framework Structure and Integrated Algorithms.

LightZero aims to promote the standardization of the MCTS+RL algorithm family to accelerate related research and applications. A performance comparison of all implemented algorithms under a unified framework is presented in the Benchmark.

Outline

• LightZero
  • News
  • 🔍 Background
  • 🎨 Overview
    • Outline
    • 💥 Features
    • 🧩 Framework Structure
    • 🎁 Integrated Algorithms
  • ⚙️ Installation
    • Installation with Docker
  • 🚀 Quick Start
  • 📚 Documentation
  • 📊 Benchmark
  • 📝 Awesome-MCTS Notes
    • Paper Notes
    • Algo. Overview
  • Awesome-MCTS Papers
    • Classic & Foundational Papers
      • LightZero Implemented series
      • AlphaGo series
      • MuZero series
      • MCTS Analysis
      • MCTS Application
    • Recent Research & Emerging Applications
      • ICML
      • ICLR
      • NeurIPS
      • Other Conference or Journal
  • 💬 Feedback and Contribution
  • 🌏 Citation
  • 💓 Acknowledgments
  • 🏷️ License

💥 Features

Lightweight: LightZero integrates multiple MCTS algorithm families and can solve decision-making problems with various attributes in a lightweight framework. The algorithms and environments LightZero implemented can be found here.

Efficient: LightZero uses mixed heterogeneous computing programming to improve computational efficiency for the most time-consuming part of MCTS algorithms.

Easy-to-understand: LightZero provides detailed documentation and algorithm framework diagrams for all integrated algorithms to help users understand the algorithm's core and compare the differences and similarities between algorithms under the same paradigm. LightZero also provides function call graphs and network structure diagrams for algorithm code implementation, making it easier for users to locate critical code. All the documentation can be found here.

🧩 Framework Structure

<p align="center"> <img src="assets/lightzero_pipeline.svg" alt="Image Description 2" width="50%" height="auto" style="margin: 0 1%;"> </p>

The above picture is the framework pipeline of LightZero. We briefly introduce the three core modules below:

Model: Model is used to define the network structure, including the __init__ function for initializing the network structure and the forward function for computing the network's forward propagation.

Policy: Policy defines the way the network is updated and interacts with the environment, including three processes: the learning process, the collecting process, and the evaluation process.

MCTS: MCTS defines the structure of the Monte Carlo search tree and the way it interacts with the Policy. The implementation of MCTS includes two languages: Python and C++, implemented in ptree and ctree, respectively.

For the file structure of LightZero, please refer to lightzero_file_structure.

🎁 Integrated Algorithms

LightZero is a library with a PyTorch implementation of MCTS algorithms (sometimes combined with cython and cpp), including:

• AlphaZero
• MuZero
• Sampled MuZero
• Stochastic MuZero
• EfficientZero
• Gumbel MuZero
• ReZero
• UniZero

The environments and algorithms currently supported by LightZero are shown in the table below:

| Env./Algo. | AlphaZero | MuZero | Sampled MuZero | EfficientZero | Sampled EfficientZero | Gumbel MuZero | Stochastic MuZero | UniZero | Sampled UniZero | ReZero | |------------------------| -------- | ---- |---------------| ---------- | ------------------ | ------------- | ---------------- | ------- | --- | ------ | | TicTacToe | ✔ | ✔ | 🔒 | 🔒 | 🔒 | ✔ | 🔒 | ✔ | 🔒 | 🔒 | | Gomok