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The present study aims to identify potential collaboration partners. If interested in this research project, please feel free to contact our office at CASIA: [email protected].

我们（中科院自动化研究所，群体智能团队）欢迎来自各院所的合作伙伴，可分享拓展资源（地图，智能体建模等），请通过以下邮箱联系我们：[email protected]，[email protected]

Unreal-MAP (Previous name: UHMP)

This is Unreal-based Multi-Agent Playground (U-Map, previous project name is UHMP for Hybrid Unreal-based Multi-agent Playground)

Here you can use all the capabilities of Unreal Engine (Blueprints, Behavior tree, Physics engine, AI navigation, 3D models/animations and Plugin resources, etc) to build elegant (but also computational efficient) and magnificent (but also experimentally reproducible) multi-agent environments.

Developed with Unreal Engine, documenting is in process. 基于UnrealEngine开发，文档正在完善中。

Please star the Github project. Your encouragement is extremely important to us as researchers: https://github.com/binary-husky/unreal-hmp 此项目处于活跃开发阶段，请打星关注哦。

Intro 简介

Unreal-based Multi-Agent Playground (U-Map, previously called UHMP) is a new generation of multi-agent environment simulation environment based on the Unreal Engine. This platform supports adversial training between swarms & algorithms, and it is the first (and currently the only) Extensible RL environment based on the Unreal Engine to support multi-team training. U-Map is oriented to adversarial heterogeneous multi-agent reinforcement learning scenarios. The interface is written in Python, The Unreal Engine part uses C++ to handle the communication interface with Python, and other parts use blueprints. The project introduces libs such as xtensor to accelerate the mathematical operations of C++. In terms of scientific research and experiment:

  • Pure computing mode that can be compiled into Headless (i.e. dedicated server for training)
  • Simulation acceleration at any rate can be achieved until the CPU burns
  • Strong repeatability. We have already solved various butterfly effect factors in Unreal Engine that would cause unrepeatable experiments when repeating random seed.
  • Support large-scale Swarm. The communication protocol with Python is highly optimized to avoid IO jam caused by the increase of the number of agents
  • Very efficient, extremely CPU efficient. The Unreal Engine itself is far more efficient than expected.
  • Cross platform. Whether Windows, Linux, or MacOs can compile Headless mode and rendering mode clients
  • You can connect the headless process in training across OS, and even watch the environment in training

Unreal-based Multi-Agent Playground (U-Map, 之前的名称是UHMP) 是基于虚幻引擎的新一代多智能体环境仿真环境。 该平台支持多队伍对抗，为第一个（也是目前为止唯一一个）基于虚幻引擎的多智能体+多队伍强化学习环境。 U-Map面向对抗性异构多智能体强化学习场景。 接口部分采用Python编写， 虚幻引擎部分采用C++处理与Python的通讯接口，其他部分采用蓝图。 项目引入xtensor用于加速C++部分的数学运算。 在科研实验方面：

  • 可编译为Headless的纯计算模式（即dedicated server，用于训练）
  • 可实现任意倍率的仿真加速，直到跑满CPU
  • 可重复性强。排除了UnrealEngine中各种会造成实验不可重复的蝴蝶效应因素
  • 支持大规模。与Python端的通讯协议高度优化，避免了随智能体数量增多导致的IO卡顿
  • 非常高效，极其节省CPU。Unreal引擎本身的效率远超预想。
  • 跨平台。不管是Windows、Linux还是MacOs都能编译Headless模式和渲染模式的客户端
  • 可跨OS连接训练中的Headless进程，甚至可以观看训练中的环境.

Install 安装方法

  • Step 1, you must install the Unreal Engine from the source code. For details, see the official document of the Unreal Engine: https://docs.unrealengine.com/4.27/zh-CN/ProductionPipelines/DevelopmentSetup/BuildingUnrealEngine/
  • Step 2: Clone the git resp git clone https://github.com/binary-husky/unreal-hmp.git
  • Step 3: Download large files that github cannot manage. Run python Please_ Run_ This_ First_ To_ Fetch_ Big_ Files.py
  • Step 4: Right click the UHMP.upproject downloaded in step 3, select switch unreal engine version, and then select source build at xxxxx to confirm. Then open the generated UHMP. sln and compile it
  • Finally, double-click UHMP. upproject to enter the Unreal Engine Editor.

Note that steps 1 and 4 are difficult. It is recommended to refer to the following video (the 0:00->1:46 in the video is the steps 1, and 1:46->end is steps 4): https://ageasga-my.sharepoint.com/:v:/g/personal/fuqingxu_yiteam_tech/EawfqsV2jF5Nsv3KF7X1-woBH-VTvELL6FSRX4cIgUboLg?e=Vmp67E

  • 第1步，必须从源代码安装虚幻引擎，具体方法见虚幻引擎的官方文档：https://docs.unrealengine.com/4.27/zh-CN/ProductionPipelines/DevelopmentSetup/BuildingUnrealEngine/
  • 第2步，克隆本仓库。git clone https://github.com/binary-husky/unreal-hmp.git
  • 第3步，下载github不能管理的大文件。运行python Please_Run_This_First_To_Fetch_Big_Files.py。
  • 第4步，右击第3步下载得到的UHMP.uproject，选择switch unreal engine version，再选择source build at xxxxx确认。然后打开生成的UHMP.sln，编译即可。
  • 最后，双击UHMP.uproject进入虚幻引擎编辑器。

注意，第1步和第4步较难，建议参考以下视频（视频中前1分46秒为第1步流程，后面为第4步流程）： https://ageasga-my.sharepoint.com/:v:/g/personal/fuqingxu_yiteam_tech/EawfqsV2jF5Nsv3KF7X1-woBH-VTvELL6FSRX4cIgUboLg?e=Vmp67E

Only install compiled binary 直接安装编译后的二进制客户端

https://github.com/binary-husky/hmp2g/blob/master/ZDOCS/use_unreal_hmap.md

Tutorial 环境设计方法

The document is being improved. For the video tutorial of simple demo, see EnvDesignTutorial.pptx (you need to complete step 3 of installation to download this pptx file)

Directory:

  • Chapter I. Unreal Engine
    • Build a map (Level) https://www.bilibili.com/video/BV1U24y1D7i4/?spm_id_from=333.999.0.0&vd_source=e3bc3eddd1d2414cb64ae72b6a64df55
    • Establish Agent Actor
    • Design agent blueprint program logic
    • Episode key event notification mechanism
    • Define Custom actions (Unreal Engine side)
    • The Python side controls the custom parameters of the agent
  • Chapter II. Python Interface
    • Create a task file (SubTask)
    • Modify agent initialization code
    • Modify the agent reward code
    • Select the control algorithm of each team
    • Full closed loop debugging method
  • Chapter III. Appendix
    • Headless acceleration and cross-compiling Linux package
    • Define Custom actions (Need to be familiar with the full closed-loop debugging method first)
      • Draft a list of actions
      • Python side action generation
      • UE-side action parse and execution
      • Action discretization
    • Installation guide for cross compilation tool chain

文档正在完善，简单demo的视频教程见EnvDesignTutorial.pptx（需要完成安装步骤3以下载此pptx文件）

设计方法目录：

  • 第一章 虚幻引擎部分

    • 1.1 建立地图（Level）: https://www.bilibili.com/video/BV1U24y1D7i4/?spm_id_from=333.999.0.0&vd_source=e3bc3eddd1d2414cb64ae72b6a64df55
    • 1.2 建立智能体蓝图（Agent Actor）
    • 1.3 设计智能体蓝图程序逻辑
    • 1.4 Episode关键事件通知机制
    • 1.5 自定义动作（虚幻引擎侧）（见第三章）
    • 1.6 由Python端控制Agent的自定义参数

  • 第二章 Python接口部分

    • 2.1 建立任务文件（SubTask）
    • 2.2 修改智能体初始化代码
    • 2.3 修改智能体奖励代码
    • 2.4 选择各队伍的控制算法
    • 2.5 全闭环调试方法（Python-UMAP回环）

  • 第三章 附录

    • 3.1 无渲染加速与交叉编译Linux二进制包
    • 3.2 自定义动作 （需要首先熟悉2.5全闭环调试方法）
      • 3.2.1 起草动作清单
      • 3.2.2 Python侧动作生成
      • 3.2.3 UE侧动作解析与执行
      • 3.2.4 强化学习动作离散化
    • 3.3 交叉编译工具链的安装指南

Build binary 编译二进制客户端的方法

Run following scripts.

  • Among them, Render/Server represents including graphic rendering / only computing, the later is generally used for RL training.
  • Among them, Windows/linux represents the target operating system. Note that you need to install Unreal Engine Cross Compilation Tool to compile Linux programs on Windows.

运行一下脚本即可。

  • 其中Render/Server代表包含图形渲染/无界面仅计算，后者一般用于RL训练。
  • 其中Win/linux代表目标操作系统，注意在windows上编译linux程序需要安装虚幻引擎交叉编译工具。
python BuildlinuxRender.py
python BuildLinuxServer.py
python BuildWinRender.py
python BuildWinServer.py

  • After adding new ActionSets in Content/Assets/DefAction/ParseAction.uasset. You may encounter Ensure condition failed: !FindPin(FFunctionEntryHelper::GetWorldContextPinName()) error during packaging, if so, find and remove an extra blueprint function parameter named __WorldContext that you created by accident in ParseAction.uasset. 如果在添加新的自定义动作之后遇到上述错误，说明你无意间添加了一个叫__WorldContext的蓝图函数参数，找到并删除它即可。 https://forums.unrealengine.com/t/ensure-condition-failed-on-project-start/469587.

  • 如果在迁移项目后发生BuildCMakeLib.Automation.cs(45,54): error CS1002，请在VS中重新生成 (Rebuild, not Build!) AutomationTool即可。https://forums.unrealengine.com/t/unreal-engine-version-4-27-2-i-get-an-error-when-trying-to-package-any-project/270627

cite this project

@misc{fu2023unrealmap,
  author = {Qingxu Fu and Tianyi Hu},
  title = {U-Map: Developing Complex Multi-Agent Reinforcement Learning Benchmarks with Unreal Engine.},
  howpublished = {\url{https://github.com/binary-husky/unreal-map/}},
  year = {2023}
}

Dev log 项目开发日志

  • 2023-10-18 版本3.14
  • 2023-4-30 版本3.8，引入标准化的高效感知模块
  • 2023-3-9 正在尝试用共享内存通讯替换tcp通讯，以提高IO效率，待上传到4.0版本
  • 2023-3-1 实现高效感知模块，待上传到4.0版本
  • 2023-2-15 版本3.7融入master分支
  • 2023-2-14 3.7上传中
  • 2023-2-14 EnvDesignTutorial.pptx中更新了自定义动作的文档
  • 2023-2-14 上传了一个微缩版的hmp代码，作为入门用的U-MAP驱动，文档待写
  • 2023-2-1 将读起来蹩脚的UHMAP缩写名称改为U-Map
  • 2023-1-8 update readme
  • 2023-12-25 covid is not a flu /(ㄒoㄒ)/
  • 2022-12-22 版本3.6融入master分支
  • 2022-12-21 解决智能体scale!=1的情况下，飞行智能体高度越来越低的问题
  • 2022-12-21 修复超大规模智能体数量情况下缓存区溢出的问题
  • 2022-12-18 优化大文件下载脚本
  • 2022-12-17 版本3.5融入master分支