Imitation learning based on entropy-regularized forward and inverse reinforcement learning

17 Aug 2020Eiji UchibeKenji Doya

This paper proposes Entropy-Regularized Imitation Learning (ERIL), which is a combination of forward and inverse reinforcement learning under the framework of the entropy-regularized Markov decision process. ERIL minimizes the reverse Kullback-Leibler (KL) divergence between two probability distributions induced by a learner and an expert... (read more)

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