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researcher

S. Zhan

15 papers hereh-index 6210 citations17 works total

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

author position
  • first author5
  • middle author10

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.LG9
  • cs.AI2
  • cs.CV1
  • cs.IR1
  • cs.RO1
  • eess.SY1
same name
  • S. Zhan — 6 papers, h 3
  • S. Zhan — 3 papers, h 2
  • S. Zhan — 2 papers, h 4
  • S. Zhan — 2 papers, h 2
  • S. Zhan — 2 papers, h 22
  • S. Zhan — 1 paper, h 2

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
20222026
most citedEnforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments

8 citations · 17 across the 15 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.LG2024

Inverse Delayed Reinforcement Learning

Simon Sinong Zhan, Qingyuan Wu, Zhian Ruan +6

Inverse Reinforcement Learning (IRL) has demonstrated effectiveness in a variety of imitation tasks. In this paper, we introduce an IRL framework designed to extract rewarding feat…

cs.LG2024

Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping

Simon Sinong Zhan, Philip Wang, Qingyuan Wu +4

In this paper, we aim to tackle the limitation of the Adversarial Inverse Reinforcement Learning (AIRL) method in stochastic environments where theoretical results cannot hold and…

cs.LG2024

Variational Delayed Policy Optimization

Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +5

In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning…

cs.LG2024

Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays

Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +6

Reinforcement learning (RL) is challenging in the common case of delays between events and their sensory perceptions. State-of-the-art (SOTA) state augmentation techniques either s…

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