LightZero

最近更新于 2023.12.07 LightZero-v0.0.3

LightZero 是一个轻量、高效、易懂的 MCTS+RL 开源算法库。

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：

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、框架结构和集成算法。

LightZero 的目标是标准化 MCTS 算法族，以加速相关研究和应用。 Benchmark 中介绍了目前所有已实现算法的性能比较。

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。

集成算法

LightZero 是基于 PyTorch 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 实现。目前 LightZero 中集成的算法包括：

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：

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。

下载 Dockerfile：Dockerfile 位于 LightZero 仓库的根目录中。将此文件下载到您的本地机器。
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。
```
mkdir lightzero-docker
mv Dockerfile lightzero-docker/
cd lightzero-docker/
```
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。
```
docker build -t ubuntu-py38-lz:latest -f ./Dockerfile .
```
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。
```
docker run -dit --rm ubuntu-py38-lz:latest /bin/bash
```
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：
```
python ./LightZero/zoo/classic_control/cartpole/config/cartpole_muzero_config.py
```

快速开始

使用如下代码在 CartPole 环境上快速训练一个 MuZero 智能体:

cd LightZero
python3 -u zoo/classic_control/cartpole/config/cartpole_muzero_config.py

使用如下代码在 Pong 环境上快速训练一个 MuZero 智能体：

cd LightZero
python3 -u zoo/atari/config/atari_muzero_config.py

使用如下代码在 TicTacToe 环境上快速训练一个 MuZero 智能体：

cd LightZero
python3 -u zoo/board_games/tictactoe/config/tictactoe_muzero_bot_mode_config.py

基线算法比较

点击折叠

AlphaZero 和 MuZero 在3个棋类游戏（TicTacToe (井字棋)，Connect4 和 Gomoku (五子棋)）上的基线结果：
MuZero，MuZero w/ SSL，EfficientZero 和 Sampled EfficientZero 在3个代表性的 Atari 离散动作空间环境上的基线结果：
Sampled EfficientZero（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，LunarLanderContinuous-v2，BipedalWalker-v3，Hopper-v3 和 Walker2d-v3）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 和 MuZero 在不同模拟次数下，在四个环境（PongNoFrameskip-v4, MsPacmanNoFrameskip-v4, Gomoku 和 LunarLanderContinuous-v2）上的基线结果：
Stochastic MuZero 和 MuZero 在具有不同随机性程度的2048环境 (num_chances=2/5) 上的基线结果：
结合不同的探索机制的 MuZero w/ SSL 在 MiniGrid 环境上的基线结果：

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

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 相关论文

以下是关于 MCTS 相关的论文集合，这一部分将会持续更新，追踪 MCTS 的前沿动态。

重要论文

(点击查看更多)

LightZero Implemented series

AlphaGo series

MuZero series

MCTS Analysis

MCTS Application

其他论文

(点击查看更多)

ICML

Scalable Safe Policy Improvement via Monte Carlo Tree Search 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 2022
- Dengwei Zhao, Shikui Tu, Lei Xu
- Key: limited amount of self-plays, path consistency (PC) optimality
- ExpEnv: Go, Othello, Gomoku
Visualizing MuZero Models 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 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 2020
- Johannes Fischer, Ömer Sahin Tas
- Key: Continuous POMDP, Particle Filter Tree, information-based reward shaping, Information Gathering.
- ExpEnv: POMDPs.jl framework
- Code
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search 2020
- Binghong Chen, Chengtao Li, Hanjun Dai, Le Song
- Key: chemical retrosynthetic planning, neural-based A*-like algorithm, ANDOR tree
- ExpEnv: USPTO datasets
- Code

ICLR

Become a Proficient Player with Limited Data through Watching Pure Videos 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 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 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 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 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
Enabling Arbitrary Translation Objectives with Adaptive Tree Search 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 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
Monte-Carlo Planning and Learning with Language Action Value Estimates 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 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 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
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees 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 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 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 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 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 2023
- Yangqing Fu, Ming Sun, Buqing Nie, Yue Gao
- Key: probability tree state abstraction, transitivity and aggregation error bound
- ExpEnv: Atari, CartPole, LunarLander, Gomoku
Planning for Sample Efficient Imitation Learning 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
Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex 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? 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 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 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 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 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 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 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 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

Other Conference or Journal

On Monte Carlo Tree Search and Reinforcement Learning Journal of Artificial Intelligence Research 2017.
Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search 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
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

@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：

特别感谢以下贡献者 @PaParaZz1, @karroyan, @nighood, @jayyoung0802, @timothijoe, @TuTuHuss, @HarryXuancy, @puyuan1996, @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。

(回到顶部)

LightZero

背景

概览

导航

特点

框架结构

集成算法

安装方法

使用 Docker 进行安装

快速开始

基线算法比较

MCTS 相关笔记

论文笔记

算法框架图

MCTS 相关论文

重要论文

LightZero Implemented series

AlphaGo series

MuZero series

MCTS Analysis

MCTS Application

其他论文

ICML

ICLR

NeurIPS

Other Conference or Journal

反馈意见和贡献

引用

致谢

许可证