Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

• URL: http://arxiv.org/abs/2006.07869v4
• Date: Tue, 9 Nov 2021 10:42:04 GMT
• Title: Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
• Authors: Georgios Papoudakis, Filippos Christianos, Lukas Sch\"afer, Stefano V. Albrecht
• Abstract summary: Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria. We provide a systematic evaluation and comparison of three different classes of MARL algorithms. Our experiments serve as a reference for the expected performance of algorithms across different learning tasks.
• Score: 11.480994804659908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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