32 citations · 48 across the 13 of their papers we have counts for
11 papers · 1 filter
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…
Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning
Huihan Liu, Changyeon Kim, Bo Liu +2
Continual learning is a long-standing challenge in robot policy learning, where a policy must acquire new skills over time without catastrophically forgetting previously learned on…
Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
Jiaheng Hu, Jay Shim, Chen Tang +4
Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving…
Differentiable Information Enhanced Model-Based Reinforcement Learning
Xiaoyuan Zhang, Xinyan Cai, Bo Liu +4
Differentiable environments have heralded new possibilities for learning control policies by offering rich differentiable information that facilitates gradient-based methods. In co…
Learning Memory Mechanisms for Decision Making through Demonstrations
William Yue, Bo Liu, Peter Stone
In Partially Observable Markov Decision Processes, integrating an agent's history into memory poses a significant challenge for decision-making. Traditional imitation learning, rel…
t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making
William Yue, Bo Liu, Peter Stone
Deep generative replay has emerged as a promising approach for continual learning in decision-making tasks. This approach addresses the problem of catastrophic forgetting by levera…