  Imitation learning is often used in addition to reinforcement learning in
environments where reward design is difficult or where the reward is sparse,
but it is difficult to be able to imitate well in unknown states from a small
amount of expert data and sampling data. Supervised learning methods such as
Behavioral Cloning do not require sampling data, but usually suffer from
distribution shift. The methods based on reinforcement learning, such as
inverse reinforcement learning and Generative Adversarial imitation learning
(GAIL), can learn from only a few expert data. However, they often need to
interact with the environment. Soft Q imitation learning (SQIL) addressed the
problems, and it was shown that it could learn efficiently by combining
Behavioral Cloning and soft Q-learning with constant rewards. In order to make
this algorithm more robust to distribution shift, we propose more efficient and
robust algorithm by adding to this method a reward function based on
adversarial inverse reinforcement learning that rewards the agent for
performing actions in status similar to the demo. We call this algorithm
Discriminator Soft Q Imitation Learning (DSQIL). We evaluated it on MuJoCo
environments.
