Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs

Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) is a promising paradigm to address the exploration-exploitation dilemma in reinforcement learning. It decomposes the source task into subgoal conditional subtasks and conducts exploration and exploitation in the subgoal space. The effectiv...

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Hauptverfasser:	Zhang, Qingyang, Yang, Yiming, Ruan, Jingqing, Xiong, Xuantang, Xing, Dengpeng, Xu, Bo
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning
Online-Zugang:	Volltext bestellen
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