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Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
[article]
2021
arXiv
pre-print
Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems. In this paper, we introduce a novel Episodic Multi-agent reinforcement learning with Curiosity-driven exploration, called EMC. We leverage an insight of popular factorized MARL algorithms that the "induced" individual Q-values, i.e., the individual utility functions used for local execution, are the embeddings of local action-observation histories, and
arXiv:2111.11032v1
fatcat:r5kbcl6imvg2rkaeczi7gnyc24