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Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
[article]
2021
arXiv
pre-print
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
arXiv:2107.06857v1
fatcat:ef7kcbzftnfy3bwchtccgmwvhy