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Multi-Agent Hierarchical Graph Attention Actor-Critic Reinforcement Learning
Tongyue Li;Dianxi Shi;Songchang Jin;Zhen Wang;Huanhuan Yang;Yang Chen
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Multi-agent systems often face challenges such as elevated communication demands and intricate interactions. We propose an innovative hierarchical graph attention actor-critic reinforcement learning method to address the issues, which uses the hierarchical graph attention to capture the relationships of cooperation or competition among agents, and the agent enables a better understand of the dynam...Show More
SVR-AVT: Scale Variation Robust Active Visual Tracking
Biao Zhang;Songchang Jin;Qianying Ouyang;Huanhuan Yang;Yuxi Zheng;Chunlian Fu;Dianxi Shi
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Active Visual Tracking (AVT) is a significant research area with extensive applications in fields such as drones and autonomous driving. AVT involves controlling camera motion based on visual observations to track target object(s). In dynamic environments, especially with the presence of distractors, AVT faces the challenge of scale variation. Existing methods struggle to effectively handle these ...Show More
Improved Communication and Collision-Avoidance in Dynamic Multi-Agent Path Finding
Jing Xie;Yongjun Zhang;Qianying Ouyang;Huanhuan Yang;Fang Dong;Dianxi Shi;Songchang Jin
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Multi-Agent Path Finding (MAPF) is a classic problem with a wide range of applications. To cope with more complex situations in reality, Dynamic MAPF (DMAPF) has received much attention. The existing DMAPF definition lacks completeness or considers too simple situations. In this paper, we comprehensively model DMAPF based on realistic scenarios. Consequently, dynamic scenarios bring many problems....Show More
Crowd Perception Communication-Based Multi- Agent Path Finding With Imitation Learning
Jing Xie;Yongjun Zhang;Huanhuan Yang;Qianying Ouyang;Fang Dong;Xinyu Guo;Songchang Jin;Dianxi Shi
Deep reinforcement learning-based Multi-Agent Path Finding (MAPF) has gained significant attention due to its remarkable adaptability to environments. Existing methods primarily leverage multi-agent communication in a fully-decentralized framework to maintain scalability while enhancing information exchange among agents. However, as the number of agents and obstacles increases, the environment bec...Show More
Independent Multi-agent Reinforcement Learning Using Common Knowledge
Haomeng Hu;Dianxi Shi;Huanhuan Yang;Yingxuan Peng;Yating Zhou;Shaowu Yang
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Many recent multi-agent reinforcement learning algorithms used centralized training with decentralized execution (CTDE), which results in a training process that relies on global information and suffers from the dimensional explosion. The independent learning (IL) approaches are simple in structure and can be more easily deployed to a wider range of multi-agent scenarios, but they can only solve r...Show More
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