25 citations · 55 across the 17 of their papers we have counts for
3 papers · 1 filter
Learning Causal Dynamics Models in Object-Oriented Environments
Zhongwei Yu, Jingqing Ruan, Dengpeng Xing
Causal dynamics models (CDMs) have demonstrated significant potential in addressing various challenges in reinforcement learning. To learn CDMs, recent studies have performed causa…
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…
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…