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PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
Sample efficiency and scalability to a large number of agents are two important goals for multi-agent reinforcement learning systems. Recent works got us closer to those goals, addressing non-stationarity of the environment from a single agent's perspective by utilizing a deep net critic which depends on all observations and actions. The critic input concatenates agent observations and actions in a user-specified order. However, since deep nets aren't permutation invariant, a permuted input
arXiv:1911.00025v1
fatcat:36asva2xfrg4bd6ad4stjgp37a