Bi-level Actor-Critic for Multi-agent Coordination

Published in AAAI2020, 2020

We proposed a novel bi-level actor-critic learning method for multi-agent reinforcement learning that allows agents to have different knowledge base, while their actions still can be executed simultaneously and distributedly, and result in Stackelberg equilibrium as the solution.

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