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arXiv · 2006.07869

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Georgios Papoudakis·Filippos Christianos·Lukas Schäfer·Stefano V. Albrecht
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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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BibTeXRIS

Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht. 2020-06-14. Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks. https://arxiv.org/abs/2006.07869

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