Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksDownload PDF

Jun 08, 2021 (edited Jul 14, 2021)NeurIPS 2021 Datasets and Benchmarks Track (Round 1)Readers: Everyone
  • Abstract: 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 consistently evaluate and compare 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~\citep{samvelyan19smac} 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.
  • Supplementary Material: zip
  • URL: https://github.com/uoe-agents/epymarl https://github.com/uoe-agents/lb-foraging https://github.com/uoe-agents/robotic-warehouse
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