9 citations · 16 across the 7 of their papers we have counts for
7 papers
OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration
Yiqin Yang, Hao Hu, Yihuan Mao +10
Preference-based reinforcement learning (PbRL) can help avoid sophisticated reward designs and align better with human intentions, showing great promise in various real-world appli…
Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving
Jin Zhang, Flood Sung, Zhilin Yang +2
In the field of large language model (LLM) post-training, the effectiveness of utilizing synthetic data generated by the LLM itself has been well-presented. However, a key question…
IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse
Siyuan Li, Hao Li, Jin Zhang +3
Humans have the ability to reuse previously learned policies to solve new tasks quickly, and reinforcement learning (RL) agents can do the same by transferring knowledge from sourc…
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
Jianhao Wang, Jin Zhang, Haozhe Jiang +3
Recent offline meta-reinforcement learning (meta-RL) methods typically utilize task-dependent behavior policies (e.g., training RL agents on each individual task) to collect a mult…
CUP: Critic-Guided Policy Reuse
Jin Zhang, Siyuan Li, Chongjie Zhang
The ability to reuse previous policies is an important aspect of human intelligence. To achieve efficient policy reuse, a Deep Reinforcement Learning (DRL) agent needs to decide wh…
Active Hierarchical Exploration with Stable Subgoal Representation Learning
Siyuan Li, Jin Zhang, Jianhao Wang +2
Goal-conditioned hierarchical reinforcement learning (GCHRL) provides a promising approach to solving long-horizon tasks. Recently, its success has been extended to more general se…
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
Jin Zhang, Jianhao Wang, Hao Hu +4
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks. Despite recent progress, efficient exploration in meta-RL re…