基于虚幻引擎的多智能体强化学习通用平台，支持自定义任务与主流算法部署。
Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement Learning
- 利用虚幻引擎资源构建可自由定制的多智能体任务环境。
- 支持从规则算法到前沿强化学习模型的全链条实验部署。
- 开源易用，适合研究者快速验证多智能体协作与竞争策略。
本文提出基于虚幻引擎（Unreal-Engine, UE）的多智能体强化学习通用平台 Unreal-MAP。该平台允许用户利用 UE 社区丰富的视觉与物理资源自由创建多智能体任务，并在其中部署当前最先进的（SOTA）多智能体强化学习算法。Unreal-MAP 在部署、修改与可视化方面具有良好的用户友好性，所有组件均开源。我们还构建了兼容第三方框架提供的从规则驱动到学习型算法的实验框架。最后，我们在 Unreal-MAP 构建的多个示例任务中部署了若干 SOTA 算法，并进行了相应的实验分析。我们认为，Unreal-MAP 能通过将现有算法与用户自定义任务紧密集成，在多智能体强化学习领域发挥重要作用，推动该领域的进步。
原文摘要 · Abstract (English)
In this paper, we propose Unreal Multi-Agent Playground (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE). Unreal-MAP allows users to freely create multi-agent tasks using the vast visual and physical resources available in the UE community, and deploy state-of-the-art (SOTA) MARL algorithms within them. Unreal-MAP is user-friendly in terms of deployment, modification, and visualization, and all its components are open-source. We also develop an experimental framework compatible with algorithms ranging from rule-based to learning-based provided by third-party frameworks. Lastly, we deploy several SOTA algorithms in example tasks developed via Unreal-MAP, and conduct corresponding experimental analyses. We believe Unreal-MAP can play an important role in the MARL field by closely integrating existing algorithms with user-customized tasks, thus advancing the field of MARL.
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