Publications (11)
Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement Learning
Tianyi Hu, Qingxu Fu, Zhiqiang Pu +2
In this paper, we propose Unreal Multi-Agent Playground (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE). Unreal-MAP allows users to freely create multi-agent…
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…
A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning
Qingxu Fu, Tenghai Qiu, Jianqiang Yi +3
SOTA multiagent reinforcement algorithms distinguish themselves in many ways from their single-agent equivalences. However, most of them still totally inherit the single-agent expl…
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…
Learning Heterogeneous Agent Cooperation via Multiagent League Training
Qingxu Fu, Xiaolin Ai, Jianqiang Yi +3
Many multiagent systems in the real world include multiple types of agents with different abilities and functionality. Such heterogeneous multiagent systems have significant practi…
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…
Heterogeneity in Multi-Agent Reinforcement Learning
Tianyi Hu, Zhiqiang Pu, Yuan Wang +3
Heterogeneity is a fundamental property in multi-agent reinforcement learning (MARL), which is closely related not only to the functional differences of agents, but also to policy…
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…
Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems
Qingxu Fu, Tenghai Qiu, Jianqiang Yi +2
When dealing with a series of imminent issues, humans can naturally concentrate on a subset of these concerning issues by prioritizing them according to their contributions to moti…
CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning
Jinyuan Feng, Chaopeng Wei, Tenghai Qiu +2
In parameter-efficient fine-tuning, mixture-of-experts (MoE), which involves specializing functionalities into different experts and sparsely activating them appropriately, has bee…
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…