30 citations · 30 across the 10 of their papers we have counts for
7 papers · 1 filter
Toward Robust Open-set Adaptation: Synapse Consolidation Inspired by Rac1/MAPK Pathways
Xiao Zhang, Tianyu Hu, Juntao Lyu +2
Large Language Models (LLMs) generalize across tasks through reusable representations and flexible reasoning, yet remain brittle in real deployment when faced with evolving tasks a…
Fewer May Be Better: Enhancing Offline Reinforcement Learning with Reduced Dataset
Yiqin Yang, Quanwei Wang, Chenghao Li +8
Offline reinforcement learning (RL) represents a significant shift in RL research, allowing agents to learn from pre-collected datasets without further interaction with the environ…
Episodic Novelty Through Temporal Distance
Yuhua Jiang, Qihan Liu, Yiqin Yang +8
Exploration in sparse reward environments remains a significant challenge in reinforcement learning, particularly in Contextual Markov Decision Processes (CMDPs), where environment…
Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach
Weichao Xu, Huaxin Pei, Jingxuan Yang +3
Recent advances in decision-making policies have led to significant progress in fields such as autonomous driving and robotics. However, testing these policies remains crucial with…
Bayesian Design Principles for Offline-to-Online Reinforcement Learning
Hao Hu, Yiqin Yang, Jianing Ye +7
Offline reinforcement learning (RL) is crucial for real-world applications where exploration can be costly or unsafe. However, offline learned policies are often suboptimal, and fu…
No Prior Mask: Eliminate Redundant Action for Deep Reinforcement Learning
Dianyu Zhong, Yiqin Yang, Qianchuan Zhao
The large action space is one fundamental obstacle to deploying Reinforcement Learning methods in the real world. The numerous redundant actions will cause the agents to make repea…
DSAC: Distributional Soft Actor-Critic for Risk-Sensitive Reinforcement Learning
Xiaoteng Ma, Junyao Chen, Li Xia +3
We present Distributional Soft Actor-Critic (DSAC), a distributional reinforcement learning (RL) algorithm that combines the strengths of distributional information of accumulated…