4 citations · 5 across the 7 of their papers we have counts for
7 papers
Policy Optimality Measurement for Multi-Vehicle Decision-Making: From Extrinsic Indicators to Intrinsic Quality
Ye Han, Lijun Zhang, Dejian Meng
Evaluating Multi-Agent Reinforcement Learning (MARL) policies in autonomous driving fundamentally relies on extrinsic statistical indicators (e.g., reward curves and success rates)…
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…
Topology Enhanced MARL for Multi-Agent Cooperative Decision-Making of CAVs
Ye Han, Lijun Zhang, Dejian Meng +1
Decentralized multi-agent cooperative decision-making in continuous environments is fundamentally bottlenecked by the curse of dimensionality, where undirected exploration typicall…
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…
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…
SPformer: A Transformer Based DRL Decision Making Method for Connected Automated Vehicles
Ye Han, Lijun Zhang, Dejian Meng +2
In mixed autonomy traffic environment, every decision made by an autonomous-driving car may have a great impact on the transportation system. Because of the complex interaction bet…
A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles
Ye Han, Lijun Zhang, Dejian Meng +3
To solve the problem of lateral and logitudinal joint decision-making of multi-vehicle cooperative driving for connected and automated vehicles (CAVs), this paper proposes a Monte…