331 citations · 844 across the 47 of their papers we have counts for
9 papers · 1 filter
FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion
Guanchen Lu, Yajuan Dun, Yi Zhou +4
Scalable reinforcement learning has popularized high-throughput sampling architectures, which significantly compresses the training time for off-policy methods in robotic locomotio…
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios
Feihong Zhang, Guojian Zhan, Bin Shuai +3
Reinforcement learning (RL), known for its self-evolution capability, offers a promising approach to training high-level autonomous driving systems. However, handling constraints r…
Guiding Reinforcement Learning with Incomplete System Dynamics
Shuyuan Wang, Jingliang Duan, Nathan P. Lawrence +4
Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on…
Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning
Haoqi Yan, Haoyuan Xu, Hongbo Gao +3
Intelligent drill boom hole-seeking is a promising technology for enhancing drilling efficiency, mitigating potential safety hazards, and relieving human operators. Most existing i…
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
Dongjie Yu, Wenjun Zou, Yujie Yang +4
Safe reinforcement learning (RL) that solves constraint-satisfactory policies provides a promising way to the broader safety-critical applications of RL in real-world problems such…
Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning
Yangang Ren, Guojian Zhan, Liye Tang +3
Intersections are quite challenging among various driving scenes wherein the interaction of signal lights and distinct traffic actors poses great difficulty to learn a wise and rob…
Encoding Distributional Soft Actor-Critic for Autonomous Driving in Multi-lane Scenarios
Jingliang Duan, Yangang Ren, Fawang Zhang +5
In this paper, we propose a new reinforcement learning (RL) algorithm, called encoding distributional soft actor-critic (E-DSAC), for decision-making in autonomous driving. Unlike…
Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Driving
Jingliang Duan, Dongjie Yu, Shengbo Eben Li +4
In this paper, we propose a new state representation method, called encoding sum and concatenation (ESC), for the state representation of decision-making in autonomous driving. Unl…
Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm
Yiting Kong, Yang Guan, Jingliang Duan +3
Merging into the highway from the on-ramp is an essential scenario for automated driving. The decision-making under the scenario needs to balance the safety and efficiency performa…