6 papers · 1 filter
Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner
Chenyou Fan, Chenjia Bai, Zhao Shan +3
Diffusion models have demonstrated their capabilities in modeling trajectories of multi-tasks. However, existing multi-task planners or policies typically rely on task-specific dem…
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
Xiaoyu Wen, Xudong Yu, Rui Yang +3
To obtain a near-optimal policy with fewer interactions in Reinforcement Learning (RL), a promising approach involves the combination of offline RL, which enhances sample efficienc…
Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning
Changhong Wang, Xudong Yu, Chenjia Bai +2
In Reinforcement Learning (RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently, offline RL provides…
Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning
Xiaoyu Wen, Chenjia Bai, Kang Xu +4
Cross-domain offline reinforcement learning leverages source domain data with diverse transition dynamics to alleviate the data requirement for the target domain. However, simply m…
Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning
Chenjia Bai, Lingxiao Wang, Jianye Hao +4
Offline Reinforcement Learning (RL) has shown promising results in learning a task-specific policy from a fixed dataset. However, successful offline RL often relies heavily on the…
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning
Xudong Yu, Chenjia Bai, Hongyi Guo +2
Offline Reinforcement Learning (RL) faces distributional shift and unreliable value estimation, especially for out-of-distribution (OOD) actions. To address this, existing uncertai…