Hierarchical Policy Learning is Sensitive to Goal Space Design

4 May 2019

Papers citing "Hierarchical Policy Learning is Sensitive to Goal Space Design"

7 / 7 papers shown

Title
CORD: Generalizable Cooperation via Role Diversity Kanefumi Matsuyama Kefan Su Jiangxing Wang Deheng Ye Zongqing Lu 40 0 0 04 Jan 2025
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning Guojian Wang Faguo Wu Xiao Zhang Ning Guo Zhiming Zheng 33 3 0 27 Dec 2023
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance C. Chang Ni Mu Jiajun Wu Ling Pan Huazhe Xu 50 7 0 05 Dec 2022
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning Riashat Islam Hongyu Zang Anirudh Goyal Alex Lamb Kenji Kawaguchi Xin-hui Li Romain Laroche Yoshua Bengio Rémi Tachet des Combes OffRL AI4CE 23 9 0 01 Nov 2022
Self-supervised Reinforcement Learning with Independently Controllable Subgoals Andrii Zadaianchuk Georg Martius Fanny Yang SSL 64 16 0 09 Sep 2021
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling Xin Ye Yezhou Yang 22 14 0 16 Oct 2020
RODE: Learning Roles to Decompose Multi-Agent Tasks Tonghan Wang Tarun Gupta Anuj Mahajan Bei Peng Shimon Whiteson Chongjie Zhang OffRL 30 39 0 04 Oct 2020