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Qi Sun

13 papers hereh-index 131.4k citations28 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author10

Across the 10 of 13 papers where every author was matched, so the position is known.

fields
  • eess.SY6
  • cs.LG5
  • cs.RO2
same name
  • Qi Sun — 36 papers, h 12
  • Qi Sun — 28 papers, h 19
  • Qi Sun — 11 papers, h 4
  • Qi Sun — 11 papers, h 7
  • Qi Sun — 7 papers, h 17
  • Qi Sun — 7 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192024
most citedDistributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors

331 citations · 798 across the 13 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.RO2021★ 2 cited

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…

cs.LG2021★ 4 cited

Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence

Yang Guan, Yangang Ren, Qi Sun +5

Decision and control are core functionalities of high-level automated vehicles. Current mainstream methods, such as functionality decomposition and end-to-end reinforcement learnin…

eess.SY2021

Recurrent Model Predictive Control

Zhengyu Liu, Jingliang Duan, Wenxuan Wang +5

This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems. Unlike traditional…

cs.LG2021

Steadily Learn to Drive with Virtual Memory

Yuhang Zhang, Yao Mu, Yujie Yang +4

Reinforcement learning has shown great potential in developing high-level autonomous driving. However, for high-dimensional tasks, current RL methods suffer from low data efficienc…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.