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Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization.

, , and . ICLR (Poster), OpenReview.net, (2019)

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Experiments with Hierarchical Reinforcement Learning of Multiple Grasping Policies., , and . ISER, volume 1 of Springer Proceedings in Advanced Robotics, page 160-172. Springer, (2016)Asynchronous force and visual feedback in teleoperative laparoscopic surgical system., , , , and . IROS, page 844-849. IEEE, (2010)Guiding Trajectory Optimization by Demonstrated Distributions., , , , , and . IEEE Robotics Autom. Lett., 2 (2): 819-826 (2017)Learning the Solution Manifold in Optimization and Its Application in Motion Planning.. CoRR, (2020)Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks., and . CoRR, (2024)Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information., , and . Neural Networks, (2022)A learning-based shared control architecture for interactive task execution., , , , , and . ICRA, page 329-335. IEEE, (2017)Trajectory planning under different initial conditions for surgical task automation by learning from demonstration., , , and . ICRA, page 6507-6513. IEEE, (2014)Hierarchical reinforcement learning of multiple grasping strategies with human instructions., , and . Adv. Robotics, 32 (18): 955-968 (2018)Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization., , and . ICLR (Poster), OpenReview.net, (2019)