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Multi-agent reinforcement learning (MARL) is a powerful tool for trainingautomated systems acting independently in a common environment. However, it canlead to sub-optimal behavior when individual incentives and group incentivesdiverge. Humans are remarkably capable at solving these social dilemmas. It isan open problem in MARL to replicate such cooperative behaviors in selfishagents. In this work, we draw upon the idea of formal contracting fromeconomics to overcome diverging incentives between agents in MARL. We proposean augmentation to a Markov game where agents voluntarily agree to bindingstate-dependent transfers of reward, under pre-specified conditions. Ourcontributions are theoretical and empirical. First, we show that thisaugmentation makes all subgame-perfect equilibria of all fully observed Markovgames exhibit socially optimal behavior, given a sufficiently rich space ofcontracts. Next, we complement our game-theoretic analysis by showing thatstate-of-the-art RL algorithms learn socially optimal policies given ouraugmentation. Our experiments include classic static dilemmas like Stag Hunt,Prisoner's Dilemma and a public goods game, as well as dynamic interactionsthat simulate traffic, pollution management and common pool resourcemanagement.

Get It in Writing: Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL