Reinforcement Learning under Model Mismatch

NeurIPS 2017 Aurko RoyHuan XuSebastian Pokutta

We study reinforcement learning under model misspecification, where we do not have access to the true environment but only to a reasonably close approximation to it. We address this problem by extending the framework of robust MDPs to the model-free Reinforcement Learning setting, where we do not have access to the model parameters, but can only sample states from it... (read more)

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