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cs.LG2025
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…
cs.LG2025
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…
cs.LG2023
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…