189 citations · 234 across the 5 of their papers we have counts for
7 papers · 1 filter
Graph Reinforcement Learning Application to Co-operative Decision-Making in Mixed Autonomy Traffic: Framework, Survey, and Challenges
Qi Liu, Xueyuan Li, Zirui Li +5
Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully au…
Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments
Qi Liu, Zirui Li, Xueyuan Li +2
An efficient and reliable multi-agent decision-making system is highly demanded for the safe and efficient operation of connected autonomous vehicles in intelligent transportation…
Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving
Jingda Wu, Zhiyu Huang, Chen Lv
To further improve the learning efficiency and performance of reinforcement learning (RL), in this paper we propose a novel uncertainty-aware model-based RL (UA-MBRL) framework, an…
Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving
Jingda Wu, Zhiyu Huang, Chao Huang +4
Due to the limited smartness and abilities of machine intelligence, currently autonomous vehicles are still unable to handle all kinds of situations and completely replace drivers.…
Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving
Zhiyu Huang, Jingda Wu, Chen Lv
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous driving. However, the low sample efficiency and difficulty of designing reward functions for D…
Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning
Zhiyu Huang, Jingda Wu, Chen Lv
Driving behavior modeling is of great importance for designing safe, smart, and personalized autonomous driving systems. In this paper, an internal reward function-based driving mo…