  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 model-based Entropy-Regularized Imitation
Learning (MB-ERIL) under the entropy-regularized Markov decision process to
reduce the number of interactions with the actual environment. MB-ERIL uses two
discriminators. A policy discriminator distinguishes the actions generated by a
robot from expert ones, and a model discriminator distinguishes the
counterfactual state transitions generated by the model from the actual ones.
We derive structured discriminators so that the learning of the policy and the
model is efficient. Computer simulations and real robot experiments show that
MB-ERIL achieves a competitive performance and significantly improves the
sample efficiency compared to baseline methods.
