Third-person imitation learning (TPIL) is a variant of generative adversarial imitation learning and can learn an expert-like policy from third-person expert demonstrations. Third-person expert demonstrations usually exist in the form of videos recorded in a third-person perspective, and there is a lack of direct correspondence with samples generated by agent. To alleviate this problem, we improve TPIL by applying image difference and variational discriminator bottleneck. Empirically, our new method has better performance than TPIL on two MuJoCo tasks, Reacher and Inverted Pendulum.
Published Date: 2020-06-02
Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print) ISBN 978-1-57735-835-0 (10 issue set)
Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2020, Association for the Advancement of Artificial Intelligence All Rights Reserved