4 papers
A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space
Hongzhi Hua, Guixuan Wen, Kaigui Wu
The research of extending deep reinforcement learning (drl) to multi-agent field has solved many complicated problems and made great achievements. However, almost all these studies…
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy
Hongzhi Hua, Kaigui Wu, Guixuan Wen
Multi-agent deep reinforcement learning has been applied to address a variety of complex problems with either discrete or continuous action spaces and achieved great success. Howev…
Optimizing Cooperative path-finding: A Scalable Multi-Agent RRT* with Dynamic Potential Fields
Jinmingwu Jiang, Kaigui Wu, Haiyang Liu +4
Cooperative path-finding in multi-agent systems demands scalable solutions to navigate agents from their origins to destinations without conflict. Despite the breadth of research,…
Cooperative Pathfinding based on memory-efficient Multi-agent RRT*
Jinmingwu Jiang, Kaigui Wu
In cooperative pathfinding problems, no-conflicts paths that bring several agents from their start location to their destination need to be planned. This problem can be efficiently…