Unsupervised pre-training has recently become the bedrock for computer vision and natural
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …

Abstract This paper introduces Elastic Decision Transformer (EDT), a significant
advancement over the existing Decision Transformer (DT) and its variants. Although DT …

Learning effective reinforcement learning (RL) policies to solve real-world complex tasks
can be quite challenging without a high-fidelity simulation environment. In most cases, we …

Offline reinforcement learning (RL) has received considerable attention in recent years due
to its attractive capability of learning policies from offline datasets without environmental …

Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining
and online finetuning, promises enhanced sample efficiency and policy performance …

Safe offline RL is a promising way to bypass risky online interactions towards safe policy
learning. Most existing methods only enforce soft constraints, ie, constraining safety …

A rich representation is key to general robotic manipulation, but existing approaches to
representation learning require large amounts of multimodal demonstrations. In this work we …

Reward function is essential in reinforcement learning (RL), serving as the guiding signal to
incentivize agents to solve given tasks, however, is also notoriously difficult to design. In …

Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by
learning policies from pre-collected datasets without interacting with the environment …

One important property of DIstribution Correction Estimation (DICE) methods is that the
solution is the optimal stationary distribution ratio between the optimized and data collection …