RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning. (arXiv:2206.01207v1 [cs.LG])

In recent years, reinforcement learning has faced several challenges in the
multi-agent domain, such as the credit assignment issue. Value function
factorization emerges as a promising way to handle the credit assignment issue
under the centralized training with decentralized execution (CTDE) paradigm.
However, existing value function factorization methods cannot deal with ad-hoc
cooperation, that is, adapting to new configurations of teammates at test time.
Specifically, these methods do not explicitly utilize the relationship between
agents and cannot adapt to different …

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