5 citations · 9 across the 7 of their papers we have counts for
7 papers
ImagineBench: Evaluating Reinforcement Learning with Large Language Model Rollouts
Jing-Cheng Pang, Kaiyuan Li, Yidi Wang +3
A central challenge in reinforcement learning (RL) is its dependence on extensive real-world interaction data to learn task-specific policies. While recent work demonstrates that l…
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
Xu-Hui Liu, Tian-Shuo Liu, Shengyi Jiang +4
Combining offline and online reinforcement learning (RL) techniques is indeed crucial for achieving efficient and safe learning where data acquisition is expensive. Existing method…
: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning
Feng Xu, Yan Yin, Xinyu Zhang +3
Alphas are pivotal in providing signals for quantitative trading. The industry highly values the discovery of formulaic alphas for their interpretability and ease of analysis, comp…
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement
Xu-Hui Liu, Feng Xu, Xinyu Zhang +5
Imitation learning aims to mimic the behavior of experts without explicit reward signals. Passive imitation learning methods which use static expert datasets typically suffer from…
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning
Xue-Kun Jin, Xu-Hui Liu, Shengyi Jiang +1
Value function estimation is an indispensable subroutine in reinforcement learning, which becomes more challenging in the offline setting. In this paper, we propose Hybrid Value Es…
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
Jing-Cheng Pang, Tian Xu, Shengyi Jiang +2
Reinforcement learning (RL) has demonstrated impressive performance in decision-making tasks like embodied control, autonomous driving and financial trading. In many decision-makin…
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning
Xu-Hui Liu, Zhenghai Xue, Jing-Cheng Pang +3
In reinforcement learning, experience replay stores past samples for further reuse. Prioritized sampling is a promising technique to better utilize these samples. Previous criteria…