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In social dilemma situations, individual rationality leads to sub-optimalgroup outcomes. Several human engagements can be modeled as a sequential(multi-step) social dilemmas. However, in contrast to humans, DeepReinforcement Learning agents trained to optimize individual rewards insequential social dilemmas converge to selfish, mutually harmful behavior. Weintroduce a status-quo loss (SQLoss) that encourages an agent to stick to thestatus quo, rather than repeatedly changing its policy. We show how agentstrained with SQLoss evolve cooperative behavior in several social dilemmamatrix games. To work with social dilemma games that have visual input, wepropose GameDistill. GameDistill uses self-supervision and clustering toautomatically extract cooperative and selfish policies from a social dilemmagame. We combine GameDistill and SQLoss to show how agents evolve sociallydesirable cooperative behavior in the Coin Game.

Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss