25 citations · 32 across the 5 of their papers we have counts for
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cs.LG2023
Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs
Qingyang Zhang, Yiming Yang, Jingqing Ruan +3
Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) is a promising paradigm to address the exploration-exploitation dilemma in reinforcement learning. It decomposes the so…
cs.LG2023★ 2 cited
Explainable Reinforcement Learning via a Causal World Model
Zhongwei Yu, Jingqing Ruan, Dengpeng Xing
Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. In this paper, we develop a novel framework for expla…