Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult. In this work, we provide a systematic evaluation and comparison of three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multi-agent learning tasks. Our experiments serve as a reference for the expected performance of algorithms across different learning tasks, and we provide insights regarding the effectiveness of different learning approaches. We open-source EPyMARL, which extends the PyMARL codebase to include additional algorithms and allow for flexible configuration of algorithm implementation details such as parameter sharing. Finally, we open-source two environments for multi-agent research which focus on coordination under sparse rewards.
Stefano V. Albrecht、Georgios Papoudakis、Lukas Sch?fer、Filippos Christianos
计算技术、计算机技术
Stefano V. Albrecht,Georgios Papoudakis,Lukas Sch?fer,Filippos Christianos.Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks[EB/OL].(2020-06-14)[2025-07-23].https://arxiv.org/abs/2006.07869.点此复制
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