Offline or batch reinforcement learning seeks to learn a near-optimal policy using history
data without active exploration of the environment. To counter the insufficient coverage and …

Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …

General-purpose robots coexisting with humans in their environment must learn to relate
human language to their perceptions and actions to be useful in a range of daily tasks …

Offline reinforcement learning (RL) can learn control policies from static datasets but, like
standard RL methods, it requires reward annotations for every transition. In many cases …

Progress in deep learning highlights the tremendous potential of utilizing diverse robotic
datasets for attaining effective generalization and makes it enticing to consider leveraging …

Offline reinforcement learning typically introduces a hierarchical structure to solve the long-
horizon problem so as to address its thorny issue of variance accumulation. Problems of …

Offline reinforcement learning (RL) enables learning a decision-making policy without
interaction with the environment. This makes it particularly beneficial in situations where …

Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill
acquisition for robotic systems. However, in practice, real-world robotic RL typically requires …

This paper is concerned with the asynchronous form of Q-learning, which applies a
stochastic approximation scheme to Markovian data samples. Motivated by the recent …

Recent research in offline reinforcement learning (RL) has demonstrated that return-
conditioned supervised learning is a powerful paradigm for decision-making problems …