Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Joel Z Leibo · Edgar Duenez-Guzman · Alexander Vezhnevets · John Agapiou · Peter Sunehag · Raphael Koster · Jayd Matyas · Charles Beattie · Igor Mordatch · Thore Graepel

[ Abstract ] [ Livestream: Visit Reinforcement Learning (Multi-agent) ] [ Paper ]
Tue 20 Jul 5 a.m. — 5:20 a.m. PDT

Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised learning benchmarks). Our contribution, Melting Pot, is a MARL evaluation suite that fills this gap and uses reinforcement learning to reduce the human labor required to create novel test scenarios. This works because one agent's behavior constitutes (part of) another agent's environment. To demonstrate scalability, we have created over 80 unique test scenarios covering a broad range of research topics such as social dilemmas, reciprocity, resource sharing, and task partitioning. We apply these test scenarios to standard MARL training algorithms, and demonstrate how Melting Pot reveals weaknesses not apparent from training performance alone.

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