  In this paper, we propose a robust imitation learning (IL) framework that
improves the robustness of IL when environment dynamics are perturbed. The
existing IL framework trained in a single environment can catastrophically fail
with perturbations in environment dynamics because it does not capture the
situation that underlying environment dynamics can be changed. Our framework
effectively deals with environments with varying dynamics by imitating multiple
experts in sampled environment dynamics to enhance the robustness in general
variations in environment dynamics. In order to robustly imitate the multiple
sample experts, we minimize the risk with respect to the Jensen-Shannon
divergence between the agent's policy and each of the sample experts. Numerical
results show that our algorithm significantly improves robustness against
dynamics perturbations compared to conventional IL baselines.
