Model-Based Imitation Learning Using Entropy Regularization of Model and Policy
Eiji Uchibe
Abstract:Approaches based on generative adversarial networks for imitation learning are promising because they are sample efficient in terms of expert demonstrations. However, training a generator requires many interactions with the actual environment because model-free reinforcement learning is adopted to update a policy. To improve the sample efficiency using model-based reinforcement learning, we propose modelbased Entropy-Regularized Imitation Learning (MB-ERIL) under the entropy-regularized Markov decision process… Show more
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