3 papers
cs.AI2025
Hybrid Differential Reward: Combining Temporal Difference and Action Gradients for Efficient Multi-Agent Reinforcement Learning in Cooperative Driving
Ye Han, Lijun Zhang, Dejian Meng +1
In multi-vehicle cooperative driving tasks involving high-frequency continuous control, traditional state-based reward functions suffer from the issue of vanishing reward differenc…
cs.AI2025
A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms
Ye Han, Lijun Zhang, Dejian Meng +1
Reinforcement learning (RL) shows great potential for optimizing multi-vehicle cooperative driving strategies through the state-action-reward feedback loop, but it still faces chal…
cs.LG2024
GITSR: Graph Interaction Transformer-based Scene Representation for Multi Vehicle Collaborative Decision-making
Xingyu Hu, Lijun Zhang, Dejian Meng +2
In this study, we propose GITSR, an effective framework for Graph Interaction Transformer-based Scene Representation for multi-vehicle collaborative decision-making in intelligent…