Reinforcement Learning in Robust Markov Decision Processes

Part of Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Shiau Hong Lim, Huan Xu, Shie Mannor


An important challenge in Markov decision processes is to ensure robustness with respect to unexpected or adversarial system behavior while taking advantage of well-behaving parts of the system. We consider a problem setting where some unknown parts of the state space can have arbitrary transitions while other parts are purely stochastic. We devise an algorithm that is adaptive to potentially adversarial behavior and show that it achieves similar regret bounds as the purely stochastic case.