  The generative adversarial imitation learning (GAIL) has provided an
adversarial learning framework for imitating expert policy from demonstrations
in high-dimensional continuous tasks. However, almost all GAIL and its
extensions only design a kind of reward function of logarithmic form in the
adversarial training strategy with the Jensen-Shannon (JS) divergence for all
complex environments. The fixed logarithmic type of reward function may be
difficult to solve all complex tasks, and the vanishing gradients problem
caused by the JS divergence will harm the adversarial learning process. In this
paper, we propose a new algorithm named Wasserstein Distance guided Adversarial
Imitation Learning (WDAIL) for promoting the performance of imitation learning
(IL). There are three improvements in our method: (a) introducing the
Wasserstein distance to obtain more appropriate measure in the adversarial
training process, (b) using proximal policy optimization (PPO) in the
reinforcement learning stage which is much simpler to implement and makes the
algorithm more efficient, and (c) exploring different reward function shapes to
suit different tasks for improving the performance. The experiment results show
that the learning procedure remains remarkably stable, and achieves significant
performance in the complex continuous control tasks of MuJoCo.
