no code implementations • 19 Apr 2024 • Lisheng Wu, Ke Chen
Exploration efficiency poses a significant challenge in goal-conditioned reinforcement learning (GCRL) tasks, particularly those with long horizons and sparse rewards.
no code implementations • 29 Nov 2023 • Lisheng Wu, Ke Chen
In goal-conditioned reinforcement learning (GCRL), sparse rewards present significant challenges, often obstructing efficient learning.
1 code implementation • 28 Oct 2022 • Lisheng Wu, Ke Chen
In GCRL, exploring novel sub-goals is essential for the agent to ultimately find the pathway to the desired goal.
1 code implementation • NeurIPS 2019 • Minne Li, Lisheng Wu, Haitham Bou Ammar, Jun Wang
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models.
no code implementations • 5 Nov 2018 • Lisheng Wu, Minne Li, Jun Wang
Humans have consciousness as the ability to perceive events and objects: a mental model of the world developed from the most impoverished of visual stimuli, enabling humans to make rapid decisions and take actions.
no code implementations • 10 Oct 2018 • Zheng Tian, Shihao Zou, Ian Davies, Tim Warr, Lisheng Wu, Haitham Bou Ammar, Jun Wang
The auxiliary reward for communication is integrated into the learning of the policy module.
no code implementations • 9 Feb 2018 • Lihang Liu, Weiyao Lin, Lisheng Wu, Yong Yu, Michael Ying Yang
This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes.