Evaluating the Robustness of GAN-Based Inverse Reinforcement Learning Algorithms
Document typeMaster thesis
Rights accessOpen Access
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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.
DegreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2017)