1 citations · 1 across the 5 of their papers we have counts for
5 papers
Optimizing Highway Traffic Flow in Mixed Autonomy: A Multiagent Truncated Rollout Approach
Lu Liu, Chi Xie, Xi Xiong
The development of connected and autonomous vehicles (CAVs) offers substantial opportunities to enhance traffic efficiency. However, in mixed autonomy environments where CAVs coexi…
RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks
Yimin Tang, Xiao Xiong, Jingyi Xi +3
Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial for applications ranging from aerial swarms to warehouse automation.…
Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic
Yuting Wang, Lu Liu, Maonan Wang +1
The burgeoning field of autonomous driving necessitates the seamless integration of autonomous vehicles (AVs) with human-driven vehicles, calling for more predictable AV behavior a…
Combining Policy Gradient and Safety-Based Control for Autonomous Driving
Xi Xiong, Lu Liu
With the advancement of data-driven techniques, addressing continuous con-trol challenges has become more efficient. However, the reliance of these methods on historical data intro…
Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration
Ziqi Zhang, Xiao Xiong, Zifeng Zhuang +2
Studying how to fine-tune offline reinforcement learning (RL) pre-trained policy is profoundly significant for enhancing the sample efficiency of RL algorithms. However, directly f…