9 citations · 21 across the 7 of their papers we have counts for
14 papers
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
Fei Ye, Shen Zhang, Pin Wang +1
In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles. Many…
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks
Pin Wang, Hanhan Li, Ching-Yao Chan
Learning from demonstrations has made great progress over the past few years. However, it is generally data hungry and task specific. In other words, it requires a large amount of…
Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
Fei Ye, Pin Wang, Ching-Yao Chan +1
Recent advances in supervised learning and reinforcement learning have provided new opportunities to apply related methodologies to automated driving. However, there are still chal…
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
Fei Ye, Xuxin Cheng, Pin Wang +2
Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane chang…
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles
Pin Wang, Hanhan Li, Ching-Yao Chan
Reinforcement Learning algorithms have recently been proposed to learn time-sequential control policies in the field of autonomous driving. Direct applications of Reinforcement Lea…
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
Pin Wang, Dapeng Liu, Jiayu Chen +2
Making decisions in complex driving environments is a challenging task for autonomous agents. Imitation learning methods have great potentials for achieving such a goal. Adversaria…