Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement Learning
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). First posted on arXiv 20 Mar 2025.
Builds on
0 of 0 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation
- 2025An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks
- 2024UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI
- 2025A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games
- 2025Multi-Agent Craftax: Benchmarking Open-Ended Multi-Agent Reinforcement Learning at the Hyperscale
- 2023A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem
- 2025Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
- 2026MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning