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MatrixWorld: A pursuit-evasion platform for safe multi-agent coordination and autocurricula
- Publication Year :
- 2023
-
Abstract
- Multi-agent reinforcement learning (MARL) achieves encouraging performance in solving complex tasks. However, the safety of MARL policies is one critical concern that impedes their real-world applications. Popular multi-agent benchmarks focus on diverse tasks yet provide limited safety support. Therefore, this work proposes a safety-constrained multi-agent environment: MatrixWorld, based on the general pursuit-evasion game. Particularly, a safety-constrained multi-agent action execution model is proposed for the software implementation of safe multi-agent environments based on diverse safety definitions. It (1) extends the vertex conflict among homogeneous / cooperative agents to heterogeneous / adversarial settings, and (2) proposes three types of resolutions for each type of conflict, aiming at providing rational and unbiased feedback for safe MARL. Besides, MatrixWorld is also a lightweight co-evolution framework for the learning of pursuit tasks, evasion tasks, or both, where more pursuit-evasion variants can be designed based on different practical meanings of safety. As a brief survey, we review and analyze the co-evolution mechanism in the multi-agent setting, which clearly reveals its relationships with autocurricula, self-play, arms races, and adversarial learning. Thus, MatrixWorld can also serve as the first environment for autocurricula research, where ideas can be quickly verified and well understood.
- Subjects :
- Computer Science - Multiagent Systems
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2307.14854
- Document Type :
- Working Paper