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20182021
most citedQuadratic Q-network for Learning Continuous Control for Autonomous Vehicles

9 citations · 20 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.LG2021★ 1 cited

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…

cs.LG2020

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…

cs.LG2020

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…

cs.LG2019★ 9 cited

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…

cs.LG2019

Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

Long Xin, Pin Wang, Ching-Yao Chan +3

As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possib…