Evaluating the Robustness of GAN-Based Inverse Reinforcement Learning Algorithms
Johannes Heidecke · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · 2019
We evaluate the robustness of reward functions learned with IRL, when transferred to similar tasks. We exceed state of the art results for one benchmark task and solve another one for the first time. Modifications are proposed that achieve faster and more stable training.