# LightZero
--- [![Twitter](https://img.shields.io/twitter/url?style=social&url=https%3A%2F%2Ftwitter.com%2Fopendilab)](https://twitter.com/opendilab) [![PyPI](https://img.shields.io/pypi/v/LightZero)](https://pypi.org/project/LightZero/) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/LightZero) ![Loc](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/HansBug/e002642132ec758e99264118c66778a4/raw/loc.json) ![Comments](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/HansBug/e002642132ec758e99264118c66778a4/raw/comments.json) [![Code Test](https://github.com/opendilab/LightZero/workflows/Code%20Test/badge.svg)](https://github.com/opendilab/LightZero/actions?query=workflow%3A%22Code+Test%22) [![Badge Creation](https://github.com/opendilab/LightZero/workflows/Badge%20Creation/badge.svg)](https://github.com/opendilab/LightZero/actions?query=workflow%3A%22Badge+Creation%22) [![Package Release](https://github.com/opendilab/LightZero/workflows/Package%20Release/badge.svg)](https://github.com/opendilab/LightZero/actions?query=workflow%3A%22Package+Release%22) ![GitHub Org's stars](https://img.shields.io/github/stars/opendilab) [![GitHub stars](https://img.shields.io/github/stars/opendilab/LightZero)](https://github.com/opendilab/LightZero/stargazers) [![GitHub forks](https://img.shields.io/github/forks/opendilab/LightZero)](https://github.com/opendilab/LightZero/network) ![GitHub commit activity](https://img.shields.io/github/commit-activity/m/opendilab/LightZero) [![GitHub issues](https://img.shields.io/github/issues/opendilab/LightZero)](https://github.com/opendilab/LightZero/issues) [![GitHub pulls](https://img.shields.io/github/issues-pr/opendilab/LightZero)](https://github.com/opendilab/LightZero/pulls) [![Contributors](https://img.shields.io/github/contributors/opendilab/LightZero)](https://github.com/opendilab/LightZero/graphs/contributors) [![GitHub license](https://img.shields.io/github/license/opendilab/LightZero)](https://github.com/opendilab/LightZero/blob/master/LICENSE) 最近更新于 2024.03.15 LightZero-v0.0.4 > LightZero 是一个轻量、高效、易懂的 MCTS+RL 开源算法库。 [English](https://github.com/opendilab/LightZero/blob/main/README.md) | 简体中文 | [论文链接](https://arxiv.org/pdf/2310.08348.pdf) ## 背景 以 AlphaZero, MuZero 为代表的结合蒙特卡洛树搜索 (Monte Carlo Tree Search, MCTS) 和深度强化学习 (Deep Reinforcemeent Learning, DRL) 的方法,在诸如围棋,Atari 等各种游戏上取得了超人的水平,也在诸如蛋白质结构预测,矩阵乘法算法寻找等科学领域取得了可喜的进展。下图为蒙特卡洛树搜索(MCTS)算法族的发展历史: ![pipeline](assets/mcts_rl_evolution_overview.png) ## 概览 **LightZero** 是一个结合了蒙特卡洛树搜索和强化学习的开源算法工具包。 它支持一系列基于 MCTS 的 RL 算法,具有以下优点: - 轻量。 - 高效。 - 易懂。 详情请参考[特点](#features)、[框架结构](#framework-structure)和[集成算法](#integrated-algorithms)。 **LightZero** 的目标是**标准化 MCTS 算法族,以加速相关研究和应用。** [Benchmark](#benchmark) 中介绍了目前所有已实现算法的性能比较。 ### 导航 - [概览](#概览) - [导航](#导航) - [特点](#特点) - [框架结构](#框架结构) - [集成算法](#集成算法) - [安装方法](#安装方法) - [快速开始](#快速开始) - [基线算法比较](#基线算法比较) - [MCTS相关笔记](#MCTS-相关笔记) - [论文笔记](#论文笔记) - [算法框架图](#算法框架图) - [MCTS相关论文](#MCTS-相关论文) - [重要论文](#重要论文) - [其他论文](#其他论文) - [反馈意见和贡献](#反馈意见和贡献) - [引用](#引用) - [致谢](#致谢) - [许可证](#许可证) ### 特点 **轻量**:LightZero 中集成了多种 MCTS 族算法,能够在同一框架下轻量化地解决多种属性的决策问题。 **高效**:LightZero 针对 MCTS 族算法中耗时最长的环节,采用混合异构计算编程提高计算效率。 **易懂**:LightZero 为所有集成的算法提供了详细文档和算法框架图,帮助用户理解算法内核,在同一范式下比较算法之间的异同。同时,LightZero 也为算法的代码实现提供了函数调用图和网络结构图,便于用户定位关键代码。 ### 框架结构

Image Description 2

上图是 LightZero 的框架流程图。我们在下面简介其中的3个核心模块: **Model**: ``Model`` 用于定义网络结构,包含``__init__``函数用于初始化网络结构,和``forward``函数用于计算网络的前向传播。 **Policy**: ``Policy`` 定义了对网络的更新方式和与环境交互的方式,包括三个过程,分别是训练过程(learn)、采样过程(collect)和评估过程(evaluate)。 **MCTS**: ``MCTS`` 定义了蒙特卡洛搜索树的结构和与``Policy``的交互方式。``MCTS``的实现包括 python 和 cpp 两种,分别在``ptree``和``ctree``中实现。 关于 LightZero 的文件结构,请参考 [lightzero_file_structure](https://github.com/opendilab/LightZero/blob/main/assets/lightzero_file_structure.svg)。 ### 集成算法 LightZero 是基于 [PyTorch](https://pytorch.org/) 实现的 MCTS 算法库,在 MCTS 的实现中也用到了 cython 和 cpp。同时,LightZero 的框架主要基于 [DI-engine](https://github.com/opendilab/DI-engine) 实现。目前 LightZero 中集成的算法包括: - [AlphaZero](https://www.science.org/doi/10.1126/science.aar6404) - [MuZero](https://arxiv.org/abs/1911.08265) - [Sampled MuZero](https://arxiv.org/abs/2104.06303) - [Stochastic MuZero](https://openreview.net/pdf?id=X6D9bAHhBQ1) - [EfficientZero](https://arxiv.org/abs/2111.00210) - [Gumbel MuZero](https://openreview.net/pdf?id=bERaNdoegnO&) LightZero 目前支持的环境及算法如下表所示: | Env./Algo. | AlphaZero | MuZero | EfficientZero | Sampled EfficientZero | Gumbel MuZero | Stochastic MuZero | |---------------| -------- | ------ |-------------| ------------------ | ---------- |----------------| | TicTacToe | ✔ | ✔ | 🔒 | 🔒 | ✔ | 🔒 | | Gomoku | ✔ | ✔ | 🔒 | 🔒 | ✔ | 🔒 | | Connect4 | ✔ | ✔ | 🔒 | 🔒 | 🔒 | 🔒 | | 2048 | --- | ✔ | 🔒 | 🔒 | 🔒 | ✔ | | Chess | 🔒 | 🔒 | 🔒 | 🔒 | 🔒 | 🔒 | | Go | 🔒 | 🔒 | 🔒 | 🔒 | 🔒 | 🔒 | | CartPole | --- | ✔ | ✔ | ✔ | ✔ | ✔ | | Pendulum | --- | ✔ | ✔ | ✔ | ✔ | ✔ | | LunarLander | --- | ✔ | ✔ | ✔ | ✔ | ✔ | | BipedalWalker | --- | ✔ | ✔ | ✔ | ✔ | 🔒 | | Atari | --- | ✔ | ✔ | ✔ | ✔ | ✔ | | MuJoCo | --- | ✔ | ✔ | ✔ | 🔒 | 🔒 | | MiniGrid | --- | ✔ | ✔ | ✔ | 🔒 | 🔒 | | Bsuite | --- | ✔ | ✔ | ✔ | 🔒 | 🔒 | (1): "✔" 表示对应的项目已经完成并经过良好的测试。 (2): "🔒" 表示对应的项目在等待列表中(正在进行中)。 (3): "---" 表示该算法不支持此环境。 ## 安装方法 可以用以下命令从 Github 的源码中安装最新版的 LightZero: ```bash git clone https://github.com/opendilab/LightZero.git cd LightZero pip3 install -e . ``` 请注意,LightZero 目前仅支持在 `Linux` 和 `macOS` 平台上进行编译。 我们正在积极将该支持扩展到 `Windows` 平台。 ### 使用 Docker 进行安装 我们也提供了一个Dockerfile,用于设置包含运行 LightZero 库所需所有依赖项的环境。此 Docker 镜像基于 Ubuntu 20.04,并安装了Python 3.8以及其他必要的工具和库。 以下是如何使用我们的 Dockerfile 来构建 Docker 镜像,从该镜像运行一个容器,并在容器内执行 LightZero 代码的步骤。 1. **下载 Dockerfile**:Dockerfile 位于 LightZero 仓库的根目录中。将此[文件](https://github.com/opendilab/LightZero/blob/main/Dockerfile)下载到您的本地机器。 2. **准备构建上下文**:在您的本地机器上创建一个新的空目录,将 Dockerfile 移动到此目录,并导航到此目录。这一步有助于在构建过程中避免向 Docker 守护进程发送不必要的文件。 ```bash mkdir lightzero-docker mv Dockerfile lightzero-docker/ cd lightzero-docker/ ``` 3. **构建 Docker 镜像**:使用以下命令构建 Docker 镜像。此命令应在包含 Dockerfile 的目录内运行。 ```bash docker build -t ubuntu-py38-lz:latest -f ./Dockerfile . ``` 4. **从镜像运行容器**:使用以下命令以交互模式启动一个 Bash shell 的容器。 ```bash docker run -dit --rm ubuntu-py38-lz:latest /bin/bash ``` 5. **在容器内执行 LightZero 代码**:一旦你在容器内部,你可以使用以下命令运行示例 Python 脚本: ```bash python ./LightZero/zoo/classic_control/cartpole/config/cartpole_muzero_config.py ``` ## 快速开始 使用如下代码在 [CartPole](https://gymnasium.farama.org/environments/classic_control/cart_pole/) 环境上快速训练一个 MuZero 智能体: ```bash cd LightZero python3 -u zoo/classic_control/cartpole/config/cartpole_muzero_config.py ``` 使用如下代码在 [Pong](https://gymnasium.farama.org/environments/atari/pong/) 环境上快速训练一个 MuZero 智能体: ```bash cd LightZero python3 -u zoo/atari/config/atari_muzero_config.py ``` 使用如下代码在 [TicTacToe](https://en.wikipedia.org/wiki/Tic-tac-toe) 环境上快速训练一个 MuZero 智能体: ```bash cd LightZero python3 -u zoo/board_games/tictactoe/config/tictactoe_muzero_bot_mode_config.py ``` ## 定制化文档 为希望定制环境和算法的用户,我们提供了全面的指南: - **环境定制:** [定制环境](https://github.com/opendilab/LightZero/blob/main/docs/source/tutorials/envs/customize_envs_zh.md) - **算法定制:** [定制算法](https://github.com/opendilab/LightZero/blob/main/docs/source/tutorials/algos/customize_algos_zh.md) 如有任何疑问,欢迎随时联系我们寻求帮助。 ## 基线算法比较
点击折叠 - [AlphaZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/alphazero.py) 和 [MuZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/muzero.py) 在3个棋类游戏([TicTacToe (井字棋)](https://github.com/opendilab/LightZero/blob/main/zoo/board_games/tictactoe/envs/tictactoe_env.py),[Connect4](https://github.com/opendilab/LightZero/blob/main/zoo/board_games/connect4/envs/connect4_env.py) 和 [Gomoku (五子棋)](https://github.com/opendilab/LightZero/blob/main/zoo/board_games/gomoku/envs/gomoku_env.py))上的基线结果:

tictactoe_bot-mode_main connect4_bot-mode_main gomoku_bot-mode_main

- [MuZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/muzero.py),[MuZero w/ SSL](https://github.com/opendilab/LightZero/blob/main/lzero/policy/muzero.py),[EfficientZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/efficientzero.py) 和 [Sampled EfficientZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/sampled_efficientzero.py) 在3个代表性的 [Atari](https://github.com/opendilab/LightZero/blob/main/zoo/atari/envs/atari_lightzero_env.py) 离散动作空间环境上的基线结果:

pong_main qbert_main mspacman_main mspacman_sez_K

- [Sampled EfficientZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/sampled_efficientzero.py)(包括 ``Factored/Gaussian`` 2种策略表征方法)在5个连续动作空间环境([Pendulum-v1](https://github.com/opendilab/LightZero/blob/main/zoo/classic_control/pendulum/envs/pendulum_lightzero_env.py),[LunarLanderContinuous-v2](https://github.com/opendilab/LightZero/blob/main/zoo/box2d/lunarlander/envs/lunarlander_env.py),[BipedalWalker-v3](https://github.com/opendilab/LightZero/blob/main/zoo/box2d/bipedalwalker/envs/bipedalwalker_env.py),[Hopper-v3](https://github.com/opendilab/LightZero/blob/main/zoo/mujoco/envs/mujoco_lightzero_env.py) 和 [Walker2d-v3](https://github.com/opendilab/LightZero/blob/main/zoo/mujoco/envs/mujoco_lightzero_env.py))上的基线结果: > 其中 ``Factored Policy`` 表示智能体学习一个输出离散分布的策略网络,上述5种环境手动离散化后的动作空间维度分别为11、49(7^2)、256(4^4)、64 (4^3) 和 4096 (4^6)。``Gaussian Policy``表示智能体学习一个策略网络,该网络直接输出高斯分布的参数 μ 和 σ。

pendulum_main pendulum_sez_K lunarlander_main

bipedalwalker_main hopper_main walker2d_main

- [Gumbel MuZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/gumbel_muzero.py) 和 [MuZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/muzero.py) 在不同模拟次数下,在四个环境([PongNoFrameskip-v4](https://github.com/opendilab/LightZero/blob/main/zoo/atari/envs/atari_lightzero_env.py), [MsPacmanNoFrameskip-v4]((https://github.com/opendilab/LightZero/blob/main/zoo/atari/envs/atari_lightzero_env.py)), [Gomoku](https://github.com/opendilab/LightZero/blob/main/zoo/board_games/gomoku/envs/gomoku_env.py) 和 [LunarLanderContinuous-v2](https://github.com/opendilab/LightZero/blob/main/zoo/box2d/lunarlander/envs/lunarlander_env.py))上的基线结果:

pong_gmz_ns mspacman_gmz_ns gomoku_bot-mode_gmz_ns lunarlander_gmz_ns

- [Stochastic MuZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/stochastic_muzero.py) 和 [MuZero](https://github.com/opendilab/LightZero/blob/main/lzero/policy/muzero.py) 在具有不同随机性程度的[2048环境](https://github.com/opendilab/LightZero/blob/main/zoo/game_2048/envs/game_2048_env.py) (num_chances=2/5) 上的基线结果:

2048_stochasticmz_mz mspacman_gmz_ns

- 结合不同的探索机制的 [MuZero w/ SSL](https://github.com/opendilab/LightZero/blob/main/lzero/policy/muzero.py) 在 [MiniGrid 环境](https://github.com/opendilab/LightZero/blob/main/zoo/minigrid/envs/minigrid_lightzero_env.py)上的基线结果:

keycorridors3r3_exploration fourrooms_exploration

## MCTS 相关笔记 ### 论文笔记 以下是 LightZero 中集成算法的中文详细文档:
点击折叠 [AlphaZero](https://github.com/opendilab/LightZero/blob/main/assets/paper_notes/AlphaZero.pdf) [MuZero](https://github.com/opendilab/LightZero/blob/main/assets/paper_notes/MuZero.pdf) [EfficientZero](https://github.com/opendilab/LightZero/blob/main/assets/paper_notes/EfficientZero.pdf) [SampledMuZero](https://github.com/opendilab/LightZero/blob/main/assets/paper_notes/SampledMuZero.pdf) [GumbelMuZero](https://github.com/opendilab/LightZero/blob/main/assets/paper_notes/GumbelMuZero.pdf) [StochasticMuZero](https://github.com/opendilab/LightZero/blob/main/assets/paper_notes/StochasticMuZero.pdf) [算法概览图符号表](https://github.com/opendilab/LightZero/blob/main/assets/paper_notes/NotationTable.pdf)
### 算法框架图 以下是 LightZero 中集成算法的框架概览图:
(点击查看更多) [MCTS](https://github.com/opendilab/LightZero/blob/main/assets/algo_overview/mcts_overview.pdf) [AlphaZero](https://github.com/opendilab/LightZero/blob/main/assets/algo_overview/alphazero_overview.pdf) [MuZero](https://github.com/opendilab/LightZero/blob/main/assets/algo_overview/muzero_overview.pdf) [EfficientZero](https://github.com/opendilab/LightZero/blob/main/assets/algo_overview/efficientzero_overview.pdf) [SampledMuZero](https://github.com/opendilab/LightZero/blob/main/assets/algo_overview/sampled_muzero_overview.pdf) [GumbelMuZero](https://github.com/opendilab/LightZero/blob/main/assets/algo_overview/gumbel_muzero_overview.pdf)
## MCTS 相关论文 以下是关于 **MCTS** 相关的论文集合,[这一部分](#MCTS-相关论文) 将会持续更新,追踪 MCTS 的前沿动态。 ### 重要论文
(点击查看更多) #### LightZero Implemented series - [2018 _Science_ AlphaZero: A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play](https://www.science.org/doi/10.1126/science.aar6404) - [2019 MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://arxiv.org/abs/1911.08265) - [2021 EfficientZero: Mastering Atari Games with Limited Data](https://arxiv.org/abs/2111.00210) - [2021 Sampled MuZero: Learning and Planning in Complex Action Spaces](https://arxiv.org/abs/2104.06303) - [2022 Stochastic MuZero: Plannig in Stochastic Environments with A Learned Model](https://openreview.net/pdf?id=X6D9bAHhBQ1) - [2022 Gumbel MuZero: Policy Improvement by Planning with Gumbel](https://openreview.net/pdf?id=bERaNdoegnO&) #### AlphaGo series - [2015 _Nature_ AlphaGo Mastering the game of Go with deep neural networks and tree search](https://www.nature.com/articles/nature16961) - [2017 _Nature_ AlphaGo Zero Mastering the game of Go without human knowledge](https://www.nature.com/articles/nature24270) - [2019 ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero](https://arxiv.org/abs/1902.04522) - [Code](https://github.com/pytorch/ELF) - [2023 Student of Games: A unified learning algorithm for both perfect and imperfect information games](https://www.science.org/doi/10.1126/sciadv.adg3256) #### MuZero series - [2022 Online and Offline Reinforcement Learning by Planning with a Learned Model](https://arxiv.org/abs/2104.06294) - [2021 Vector Quantized Models for Planning](https://arxiv.org/abs/2106.04615) - [2021 Muesli: Combining Improvements in Policy Optimization. ](https://arxiv.org/abs/2104.06159) #### MCTS Analysis - [2020 Monte-Carlo Tree Search as Regularized Policy Optimization](https://arxiv.org/abs/2007.12509) - [2021 Self-Consistent Models and Values](https://arxiv.org/abs/2110.12840) - [2022 Adversarial Policies Beat Professional-Level Go AIs](https://arxiv.org/abs/2211.00241) - [2022 _PNAS_ Acquisition of Chess Knowledge in AlphaZero.](https://arxiv.org/abs/2111.09259) #### MCTS Application - [2023 Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search](https://openreview.net/pdf?id=ZTK3SefE8_Z) - [2022 _Nature_ Discovering faster matrix multiplication algorithms with reinforcement learning](https://www.nature.com/articles/s41586-022-05172-4) - [Code](https://github.com/deepmind/alphatensor) - [2022 MuZero with Self-competition for Rate Control in VP9 Video Compression](https://arxiv.org/abs/2202.06626) - [2021 DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning](https://arxiv.org/abs/2106.06135) - [2019 Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving](https://arxiv.org/pdf/1905.02680.pdf)
### 其他论文
(点击查看更多) #### ICML - [Scalable Safe Policy Improvement via Monte Carlo Tree Search](https://openreview.net/pdf?id=tevbBSzSfK) 2023 - Alberto Castellini, Federico Bianchi, Edoardo Zorzi, Thiago D. Simão, Alessandro Farinelli, Matthijs T. J. Spaan - Key: safe policy improvement online using a MCTS based strategy, Safe Policy Improvement with Baseline Bootstrapping - ExpEnv: Gridworld and SysAdmin - [Efficient Learning for AlphaZero via Path Consistency](https://proceedings.mlr.press/v162/zhao22h/zhao22h.pdf) 2022 - Dengwei Zhao, Shikui Tu, Lei Xu - Key: limited amount of self-plays, path consistency (PC) optimality - ExpEnv: Go, Othello, Gomoku - [Visualizing MuZero Models](https://arxiv.org/abs/2102.12924) 2021 - Joery A. de Vries, Ken S. Voskuil, Thomas M. Moerland, Aske Plaat - Key: visualizing the value equivalent dynamics model, action trajectories diverge, two regularization techniques - ExpEnv: CartPole and MountainCar. and internal state transition dynamics, - [Convex Regularization in Monte-Carlo Tree Search](https://arxiv.org/pdf/2007.00391.pdf) 2021 - Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen - Key: entropy-regularization backup operators, regret analysis, Tsallis etropy - ExpEnv: synthetic tree, Atari - [Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains](http://proceedings.mlr.press/v119/fischer20a/fischer20a.pdf) 2020 - Johannes Fischer, Ömer Sahin Tas - Key: Continuous POMDP, Particle Filter Tree, information-based reward shaping, Information Gathering. - ExpEnv: POMDPs.jl framework - [Code](https://github.com/johannes-fischer/icml2020_ipft) - [Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search](http://proceedings.mlr.press/v119/chen20k/chen20k.pdf) 2020 - Binghong Chen, Chengtao Li, Hanjun Dai, Le Song - Key: chemical retrosynthetic planning, neural-based A*-like algorithm, ANDOR tree - ExpEnv: USPTO datasets - [Code](https://github.com/binghong-ml/retro_star) #### ICLR - [The Update Equivalence Framework for Decision-Time Planning](https://openreview.net/forum?id=JXGph215fL) 2024 - Samuel Sokota, Gabriele Farina, David J Wu, Hengyuan Hu, Kevin A. Wang, J Zico Kolter, Noam Brown - Key: imperfect-information games, search, decision-time planning, update equivalence - ExpEnv: Hanabi, 3x3 Abrupt Dark Hex and Phantom Tic-Tac-Toe - [Efficient Multi-agent Reinforcement Learning by Planning](https://openreview.net/forum?id=CpnKq3UJwp) 2024 - Qihan Liu, Jianing Ye, Xiaoteng Ma, Jun Yang, Bin Liang, Chongjie Zhang - Key: multi-agent reinforcement learning, planning, multi-agent MCTS - ExpEnv: SMAC, LunarLander, MuJoCo, and Google Research Football - [Become a Proficient Player with Limited Data through Watching Pure Videos](https://openreview.net/pdf?id=Sy-o2N0hF4f) 2023 - Weirui Ye, Yunsheng Zhang, Pieter Abbeel, Yang Gao - Key: pre-training from action-free videos, forward-inverse cycle consistency (FICC) objective based on vector quantization, pre-training phase, fine-tuning phase. - ExpEnv: Atari - [Policy-Based Self-Competition for Planning Problems](https://arxiv.org/abs/2306.04403) 2023 - Jonathan Pirnay, Quirin Göttl, Jakob Burger, Dominik Gerhard Grimm - Key: self-competition, find strong trajectories by planning against possible strategies of its past self. - ExpEnv: Traveling Salesman Problem and the Job-Shop Scheduling Problem. - [Explaining Temporal Graph Models through an Explorer-Navigator Framework](https://openreview.net/pdf?id=BR_ZhvcYbGJ) 2023 - Wenwen Xia, Mincai Lai, Caihua Shan, Yao Zhang, Xinnan Dai, Xiang Li, Dongsheng Li - Key: Temporal GNN Explainer, an explorer to find the event subsets with MCTS, a navigator that learns the correlations between events and helps reduce the search space. - ExpEnv: Wikipedia and Reddit, Synthetic datasets - [SpeedyZero: Mastering Atari with Limited Data and Time](https://openreview.net/pdf?id=Mg5CLXZgvLJ) 2023 - Yixuan Mei, Jiaxuan Gao, Weirui Ye, Shaohuai Liu, Yang Gao, Yi Wu - Key: distributed RL system, Priority Refresh, Clipped LARS - ExpEnv: Atari - [Efficient Offline Policy Optimization with a Learned Model](https://openreview.net/pdf?id=Yt-yM-JbYFO) 2023 - Zichen Liu, Siyi Li, Wee Sun Lee, Shuicheng YAN, Zhongwen Xu - Key: Regularized One-Step Model-based algorithm for Offline-RL - ExpEnv: Atari,BSuite - [Code](https://github.com/sail-sg/rosmo/tree/main) - [Enabling Arbitrary Translation Objectives with Adaptive Tree Search](https://arxiv.org/pdf/2202.11444.pdf) 2022 - Wang Ling, Wojciech Stokowiec, Domenic Donato, Chris Dyer, Lei Yu, Laurent Sartran, Austin Matthews - Key: adaptive tree search, translation models, autoregressive models, - ExpEnv: Chinese–English and Pashto–English tasks from WMT2020, German–English from WMT2014 - [What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization](https://arxiv.org/abs/2201.10494) 2022 - Maximili1an Böther, Otto Kißig, Martin Taraz, Sarel Cohen, Karen Seidel, Tobias Friedrich - Key: Combinatorial optimization, open-source benchmark suite for the NP-hard MAXIMUM INDEPENDENT SET problem, an in-depth analysis of the popular guided tree search algorithm, compare the tree search implementations to other solvers - ExpEnv: NP-hard MAXIMUM INDEPENDENT SET. - [Code](https://github.com/maxiboether/mis-benchmark-framework) - [Monte-Carlo Planning and Learning with Language Action Value Estimates](https://openreview.net/pdf?id=7_G8JySGecm) 2021 - Youngsoo Jang, Seokin Seo, Jongmin Lee, Kee-Eung Kim - Key: Monte-Carlo tree search with language-driven exploration, locally optimistic language value estimates, - ExpEnv: Interactive Fiction (IF) games - [Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design](https://arxiv.org/abs/2006.10504) 2021 - Xiufeng Yang, Tanuj Kr Aasawat, Kazuki Yoshizoe - Key: massively parallel Monte-Carlo Tree Search, molecular design, Hash-driven parallel search, - ExpEnv: octanol-water partition coefficient (logP) penalized by the synthetic accessibility (SA) and large Ring Penalty score. - [Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search](https://arxiv.org/pdf/1810.11755.pdf) 2020 - Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou, Ji Liu - Key: parallel Monte-Carlo Tree Search, partition the tree into sub-trees efficiently, compare the observation ratio of each processor - ExpEnv: speedup and performance comparison on JOY-CITY game, average episode return on atari game - [Code](https://github.com/liuanji/WU-UCT) - [Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees](https://openreview.net/pdf?id=rJgJDAVKvB) 2020 - Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song - Key: meta path planning algorithm, exploits a novel neural architecture which can learn promising search directions from problem structures. - ExpEnv: a 2d workspace with a 2 DoF (degrees of freedom) point robot, a 3 DoF stick robot and a 5 DoF snake robot #### NeurIPS - [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://openreview.net/pdf?id=oIUXpBnyjv) 2023 - Yazhe Niu, Yuan Pu, Zhenjie Yang, Xueyan Li, Tong Zhou, Jiyuan Ren, Shuai Hu, Hongsheng Li, Yu Liu - Key: the first unified benchmark for deploying MCTS/MuZero in general sequential decision scenarios. - ExpEnv: ClassicControl, Box2D, Atari, MuJoCo, GoBigger, MiniGrid, TicTacToe, ConnectFour, Gomoku, 2048, etc. - [Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://openreview.net/pdf?id=Wjp1AYB8lH) 2023 - Zirui Zhao, Wee Sun Lee, David Hsu - Key: world model (LLM) and the LLM-induced policy can be combined in MCTS, to scale up task planning. - ExpEnv: multiplication, travel planning, object rearrangement - [Monte Carlo Tree Search with Boltzmann Exploration](https://openreview.net/pdf?id=NG4DaApavi) 2023 - Michael Painter, Mohamed Baioumy, Nick Hawes, Bruno Lacerda - Key: Boltzmann exploration with MCTS, optimal actions for the maximum entropy objective do not necessarily correspond to optimal actions for the original objective, two improved algorithms. - ExpEnv: the Frozen Lake environment, the Sailing Problem, Go - [Generalized Weighted Path Consistency for Mastering Atari Games](https://openreview.net/pdf?id=vHRLS8HhK1) 2023 - Dengwei Zhao, Shikui Tu, Lei Xu - Key: Generalized Weighted Path Consistency, A weighting mechanism. - ExpEnv: Atari - [Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction](https://openreview.net/pdf?id=0zeLTZAqaJ) 2023 - Yangqing Fu, Ming Sun, Buqing Nie, Yue Gao - Key: probability tree state abstraction, transitivity and aggregation error bound - ExpEnv: Atari, CartPole, LunarLander, Gomoku - [Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions](https://openreview.net/pdf?id=B_LdLljS842) 2022 - Weirui Ye, Pieter Abbeel, Yang Gao - Key: trade off computation versus performancem, virtual expansions, spend thinking time adaptively. - ExpEnv: Atari, 9x9 Go - [Planning for Sample Efficient Imitation Learning](https://openreview.net/forum?id=BkN5UoAqF7) 2022 - Zhao-Heng Yin, Weirui Ye, Qifeng Chen, Yang Gao - Key: Behavioral Cloning,Adversarial Imitation Learning (AIL),MCTS-based RL, - ExpEnv: DeepMind Control Suite - [Code](https://github.com/zhaohengyin/EfficientImitate) - [Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex](https://openreview.net/pdf?id=dwKwB2Cd-Km) 2022 - Charles Lovering, Jessica Zosa Forde, George Konidaris, Ellie Pavlick, Michael L. Littman - Key: AlphaZero’s internal representations, model probing and behavioral tests, how these concepts are captured in the network. - ExpEnv: Hex - [Are AlphaZero-like Agents Robust to Adversarial Perturbations?](https://openreview.net/pdf?id=yZ_JlZaOCzv) 2022 - Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, 4 Cho-Jui Hsieh - Key: adversarial states, first adversarial attack on Go AIs - ExpEnv: Go - [Monte Carlo Tree Descent for Black-Box Optimization](https://openreview.net/pdf?id=FzdmrTUyZ4g) 2022 - Yaoguang Zhai, Sicun Gao - Key: Black-Box Optimization, how to further integrate samplebased descent for faster optimization. - ExpEnv: synthetic functions for nonlinear optimization, reinforcement learning problems in MuJoCo locomotion environments, and optimization problems in Neural Architecture Search (NAS). - [Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization](https://openreview.net/pdf?id=SUzPos_pUC) 2022 - Lei Song∗ , Ke Xue∗ , Xiaobin Huang, Chao Qian - Key: a low-dimensional subspace via MCTS, optimizes in the subspace with any Bayesian optimization algorithm. - ExpEnv: NAS-bench problems and MuJoCo locomotion - [Monte Carlo Tree Search With Iteratively Refining State Abstractions](https://proceedings.neurips.cc/paper/2021/file/9b0ead00a217ea2c12e06a72eec4923f-Paper.pdf) 2021 - Samuel Sokota, Caleb Ho, Zaheen Ahmad, J. Zico Kolter - Key: stochastic environments, Progressive widening, abstraction refining, - ExpEnv: Blackjack, Trap, five by five Go. - [Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess](https://proceedings.neurips.cc/paper/2021/file/215a71a12769b056c3c32e7299f1c5ed-Paper.pdf) 2021 - Gregory Clark - Key: imperfect information, belief state with an unweighted particle filter, a novel stochastic abstraction of information states. - ExpEnv: reconnaissance blind chess - [POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis](https://proceedings.neurips.cc/paper/2020/file/30de24287a6d8f07b37c716ad51623a7-Paper.pdf) 2020 - Weichao Mao, Kaiqing Zhang, Qiaomin Xie, Tamer Ba¸sar - Key: continuous state-action spaces, Hierarchical Optimistic Optimization, - ExpEnv: CartPole, Inverted Pendulum, Swing-up, and LunarLander. - [Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search](https://proceedings.neurips.cc/paper/2020/file/e2ce14e81dba66dbff9cbc35ecfdb704-Paper.pdf) 2020 - Linnan Wang, Rodrigo Fonseca, Yuandong Tian - Key: learns the partition of the search space using a few samples, a nonlinear decision boundary and learns a local model to pick good candidates. - ExpEnv: MuJoCo locomotion tasks, Small-scale Benchmarks, - [Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions](https://arxiv.org/abs/1907.10154) 2020 - Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai - Key: covariate shift problem, Mix&Match combines stochastic gradient descent (SGD) with optimistic tree search and model re-use (evolving partially trained models with samples from different mixture distributions) - [Code](https://github.com/matthewfaw/mixnmatch) #### Other Conference or Journal - [Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search](https://arxiv.org/pdf/2012.07910.pdf) AAAI 2021. - [On Monte Carlo Tree Search and Reinforcement Learning](https://www.jair.org/index.php/jair/article/download/11099/26289/20632) Journal of Artificial Intelligence Research 2017. - [Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search](https://arxiv.org/pdf/1906.06832) IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
## 反馈意见和贡献 - 有任何疑问或意见都可以在 github 上直接 [提出 issue](https://github.com/opendilab/LightZero/issues/new/choose) - 或者联系我们的邮箱 (opendilab@pjlab.org.cn) - 感谢所有的反馈意见,包括对算法和系统设计。这些反馈意见和建议都会让 LightZero 变得更好。 ## 引用 ```latex @misc{lightzero, title={LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios}, author={Yazhe Niu and Yuan Pu and Zhenjie Yang and Xueyan Li and Tong Zhou and Jiyuan Ren and Shuai Hu and Hongsheng Li and Yu Liu}, year={2023}, eprint={2310.08348}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ## 致谢 此算法库的实现部分基于以下 GitHub 仓库,非常感谢这些开创性工作: - https://github.com/opendilab/DI-engine - https://github.com/deepmind/mctx - https://github.com/YeWR/EfficientZero - https://github.com/werner-duvaud/muzero-general 特别感谢以下贡献者 [@PaParaZz1](https://github.com/PaParaZz1), [@karroyan](https://github.com/karroyan), [@nighood](https://github.com/nighood), [@jayyoung0802](https://github.com/jayyoung0802), [@timothijoe](https://github.com/timothijoe), [@TuTuHuss](https://github.com/TuTuHuss), [@HarryXuancy](https://github.com/HarryXuancy), [@puyuan1996](https://github.com/puyuan1996), [@HansBug](https://github.com/HansBug) 对本项目的贡献和支持。 感谢所有为此项目做出贡献的人: ## 许可证 本仓库中的所有代码都符合 [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)。

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