POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation

Авторы

Панов А. И. Яковлев К. С. Скрынник А. А.

Аннотация

Multi-agent reinforcement learning (MARL) has recently excelled in solving challenging cooperative and competitive multi-agent problems in various environments with, mostly, few agents and full observability. Moreover, a range of crucial robotics-related tasks, such as multi-robot navigation and obstacle avoidance, that have been conventionally approached with the classical non-learnable methods (e.g., heuristic search) is currently suggested to be solved by the learning-based or hybrid methods. Still, in this domain, it is hard, not to say impossible, to conduct a fair comparison between classical, learning-based, and hybrid approaches due to the lack of a unified framework that supports both learning and evaluation. To this end, we introduce POGEMA, a set of comprehensive tools that includes a fast environment for learning, a generator of problem instances, the collection of pre-defined ones, a visualization toolkit, and a benchmarking tool that allows automated evaluation. We introduce and specify an evaluation protocol defining a range of domain-related metrics computed on the basics of the primary evaluation indicators (such as success rate and path length), allowing a fair multi-fold comparison. The results of such a comparison, which involves a variety of state-of-the-art MARL, search-based, and hybrid methods, are presented.

Внешние ссылки

DOI: 10.48550/arXiv.2407.14931

Скачать PDF или читать онлайн на arXiv.org (англ.): https://arxiv.org/html/2407.14931v1

ResearchGate: https://www.researchgate.net/publication/382459974_POGEMA_A_Benchmark_Platform_for_Cooperative_Multi-Agent_Navigation

Ссылка при цитировании

Alexey Skrynnik, Anton Andreychuk, Anatolii Borzilov, Alexander Chernyavskiy, Konstantin Yakovlev, Aleksandr Panov. POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation // arXiv:2407.14931v1 [cs.LG], 20 July 2024.