25 citations · 30 across the 6 of their papers we have counts for
6 papers
Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making
Jingqing Ruan, Kaishen Wang, Qingyang Zhang +2
Many complicated real-world tasks can be broken down into smaller, more manageable parts, and planning with prior knowledge extracted from these simplified pieces is crucial for hu…
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…
Mixture of personality improved Spiking actor network for efficient multi-agent cooperation
Xiyun Li, Ziyi Ni, Jingqing Ruan +4
Adaptive human-agent and agent-agent cooperation are becoming more and more critical in the research area of multi-agent reinforcement learning (MARL), where remarked progress has…
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…