25 citations · 32 across the 5 of their papers we have counts for
5 papers
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…
GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning
Jingqing Ruan, Yali Du, Xuantang Xiong +6
Many real-world scenarios involve a team of agents that have to coordinate their policies to achieve a shared goal. Previous studies mainly focus on decentralized control to maximi…
Learning Multi-agent Action Coordination via Electing First-move Agent
Jingqing Ruan, Linghui Meng, Xuantang Xiong +2
Learning to coordinate actions among agents is essential in complicated multi-agent systems. Prior works are constrained mainly by the assumption that all agents act simultaneously…
General Robot Dynamics Learning and Gen2Real
Dengpeng Xing, Jiale Li, Yiming Yang +1
Acquiring dynamics is an essential topic in robot learning, but up-to-date methods, such as dynamics randomization, need to restart to check nominal parameters, generate simulation…