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Temporal Difference Learning with Constrained Initial Representations
Jiafei Lyu, Jingwen Yang, Zhongjian Qiao +5
Recently, there have been numerous attempts to enhance the sample efficiency of off-policy reinforcement learning (RL) agents when interacting with the environment, including archi…
Cross-Domain Offline Policy Adaptation via Selective Transition Correction
Mengbei Yan, Jiafei Lyu, Shengjie Sun +5
It remains a critical challenge to adapt policies across domains with mismatched dynamics in reinforcement learning (RL). In this paper, we study cross-domain offline RL, where an…
Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling
Yiran Guo, Zhongjian Qiao, Yingqi Xie +5
Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast n…
Dual-Robust Cross-Domain Offline Reinforcement Learning Against Dynamics Shifts
Zhongjian Qiao, Rui Yang, Jiafei Lyu +5
Single-domain offline reinforcement learning (RL) often suffers from limited data coverage, while cross-domain offline RL handles this issue by leveraging additional data from othe…
Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering
Zhongjian Qiao, Rui Yang, Jiafei Lyu +4
Cross-domain offline reinforcement learning (RL) aims to train a well-performing agent in the target environment, leveraging both a limited target domain dataset and a source domai…
SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning
Zhongjian Qiao, Jiafei Lyu, Kechen Jiao +2
The performance of offline reinforcement learning (RL) suffers from the limited size and quality of static datasets. Model-based offline RL addresses this issue by generating synth…
Mind the Model, Not the Agent: The Primacy Bias in Model-based RL
Zhongjian Qiao, Jiafei Lyu, Xiu Li
The primacy bias in model-free reinforcement learning (MFRL), which refers to the agent's tendency to overfit early data and lose the ability to learn from new data, can significan…