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We consider multi-agent reinforcement learning (MARL) for cooperativecommunication and coordination tasks. MARL agents can be brittle because theycan overfit their training partners' policies. This overfitting can produceagents that adopt policies that act under the expectation that other agentswill act in a certain way rather than react to their actions. Our objective isto bias the learning process towards finding reactive strategies towards otheragents' behaviors. Our method, transfer empowerment, measures the potentialinfluence between agents' actions. Results from three simulated cooperationscenarios support our hypothesis that transfer empowerment improves MARLperformance. We discuss how transfer empowerment could be a useful principle toguide multi-agent coordination by ensuring reactiveness to one's partner.

Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment