We consider multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents can be brittle because they can overfit their training partners' policies. This overfitting can produce agents that adopt policies that act under the expectation that other agents will act in a certain way rather than react to their actions. Our objective is to bias the learning process towards finding reactive strategies towards other agents' behaviors. Our method, transfer empowerment, measures the potential influence between agents' actions. Results from three simulated cooperation scenarios support our hypothesis that transfer empowerment improves MARL performance. We discuss how transfer empowerment could be a useful principle to guide multi-agent coordination by ensuring reactiveness to one's partner.

本文介绍了一种名为transfer empowerment的方法，它可以通过度量一个智能体对另一个智能体的行为的潜在影响来改进多智能体协作的过程，从而使训练出的多智能体策略更加具有反应性和鲁棒性。实验结果表明，transfer empowerment可以有效提高多智能体强化学习的性能。

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