Competitive Multiagent Inverse Reinforcement Learning with Suboptimal Demonstrations
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Proceedings of the 35th International Conference on Machine Learning, PMLR 80:51435151, 2018.
Abstract
This paper considers the problem of inverse reinforcement learning in zerosum stochastic games when expert demonstrations are known to be suboptimal. Compared to previous works that decouple agents in the game by assuming optimality in expert policies, we introduce a new objective function that directly pits experts against Nash Equilibrium policies, and we design an algorithm to solve for the reward function in the context of inverse reinforcement learning with deep neural networks as model approximations. To ?nd Nash Equilibrium in largescale games, we also propose an adversarial training algorithm for zerosum stochastic games, and show the theoretical appeal of nonexistence of local optima in its objective function. In numerical experiments, we demonstrate that our Nash Equilibrium and inverse reinforcement learning algorithms address games that are not amenable to existing benchmark algorithms. Moreover, our algorithm successfully recovers reward and policy functions regardless of the quality of the suboptimal expert demonstration set.
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