1 citations · 1 across the 5 of their papers we have counts for
5 papers
Efficient Multi-Task Reinforcement Learning via Task-Specific Action Correction
Jinyuan Feng, Min Chen, Zhiqiang Pu +2
Multi-task reinforcement learning (MTRL) demonstrate potential for enhancing the generalization of a robot, enabling it to perform multiple tasks concurrently. However, the perform…
Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems
Qingxu Fu, Zhiqiang Pu, Min Chen +2
Large-scale heterogeneous multiagent systems feature various realistic factors in the real world, such as agents with diverse abilities and overall system cost. In comparison to ho…
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph
Qingxu Fu, Tenghai Qiu, Jianqiang Yi +2
Multi-Agent Reinforcement Learning (MARL) has been successful in solving many cooperative challenges. However, classic non-hierarchical MARL algorithms still cannot address various…
Measuring Policy Distance for Multi-Agent Reinforcement Learning
Tianyi Hu, Zhiqiang Pu, Xiaolin Ai +2
Diversity plays a crucial role in improving the performance of multi-agent reinforcement learning (MARL). Currently, many diversity-based methods have been developed to overcome th…
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning
Qingxu Fu, Tenghai Qiu, Zhiqiang Pu +2
Multiagent reinforcement learning (MARL) can solve complex cooperative tasks. However, the efficiency of existing MARL methods relies heavily on well-defined reward functions. Mult…