5 papers
Bidirectional Task-Motion Planning Based on Hierarchical Reinforcement Learning for Strategic Confrontation
Qizhen Wu, Lei Chen, Kexin Liu +1
In swarm robotics, confrontation scenarios, including strategic confrontations, require efficient decision-making that integrates discrete commands and continuous actions. Traditio…
Tactical Decision for Multi-UGV Confrontation with a Vision-Language Model-Based Commander
Li Wang, Qizhen Wu, Lei Chen
In multiple unmanned ground vehicle confrontations, autonomously evolving multi-agent tactical decisions from situational awareness remain a significant challenge. Traditional hand…
Hierarchical Reinforcement Learning for Swarm Confrontation with High Uncertainty
Qizhen Wu, Kexin Liu, Lei Chen +1
In swarm robotics, confrontation including the pursuit-evasion game is a key scenario. High uncertainty caused by unknown opponents' strategies, dynamic obstacles, and insufficient…
Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic Environment
Qizhen Wu, Kexin Liu, Lei Chen +1
Traditional methods plan feasible paths for multiple agents in the stochastic environment. However, the methods' iterations with the changes in the environment result in computatio…
Model predictive control-based value estimation for efficient reinforcement learning
Qizhen Wu, Kexin Liu, Lei Chen
Reinforcement learning suffers from limitations in real practices primarily due to the number of required interactions with virtual environments. It results in a challenging proble…