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Wei Liu

6 papers hereh-index 27 citations8 works total

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

author position
  • middle author4
  • last author2

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.RO3
  • cs.LG1
  • eess.SY1
  • stat.ML1
same name
  • Wei Liu — 69 papers, h 37
  • Wei Liu — 43 papers, h 27
  • Wei Liu — 35 papers, h 46
  • Wei Liu — 32 papers, h 34
  • Wei Liu — 32 papers, h 12
  • Wei Liu — 26 papers

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
20232026
collaborators
Showing 2026Show all

4 papers · 1 filter

cs.LG2026

Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark

Cristiano da Costa Cunha, Ajmal Mian, Tim French +1

Causal reinforcement learning (RL) lacks benchmarks for complex systems that combine sequential decision making, hidden information, large masked action spaces, and explicit causal…

stat.ML2026

Maximizing Rollout Informativeness under a Fixed Budget: A Submodular View of Tree Search for Tool-Use Agentic Reinforcement Learning

Yuelin Hu, Zhenbo Yu, Zhengxue Cheng +2

We formalize Rollout Informativeness under a Fixed Budget (RIFB) as the expected non-vanishing policy-gradient mass that a tool-use rollout set injects into Group Relative Policy O…

cs.RO2026

AGILE: A Comprehensive Workflow for Humanoid Loco-Manipulation Learning

Huihua Zhao, Rafael Cathomen, Lionel Gulich +6

Recent advances in reinforcement learning (RL) have enabled impressive humanoid behaviors in simulation, yet transferring these results to new robots remains challenging. In many r…

eess.SY2026

UAV Trajectory Optimization via Improved Noisy Deep Q-Network

Zhang Hengyu, Maryam Cheraghy, Liu Wei +3

This paper proposes an Improved Noisy Deep Q-Network (Noisy DQN) to enhance the exploration and stability of Unmanned Aerial Vehicle (UAV) when applying deep reinforcement learning…

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