Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence
modeling problem. This allows us to draw upon the simplicity and scalability of the …

While behavior learning has made impressive progress in recent times, it lags behind
computer vision and natural language processing due to its inability to leverage large …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

Diffusion models have emerged as powerful generative models in the text-to-image domain.
This paper studies their application as observation-to-action models for imitating human …

Recent advance in deep offline reinforcement learning (RL) has made it possible to train
strong robotic agents from offline datasets. However, depending on the quality of the trained …

We propose a new policy representation based on score-based diffusion models (SDMs).
We apply our new policy representation in the domain of Goal-Conditioned Imitation …

Fine-tuning reinforcement learning (RL) models has been challenging because of a lack of
large scale off-the-shelf datasets as well as high variance in transferability among different …

In offline reinforcement learning, weighted regression is a common method to ensure the
learned policy stays close to the behavior policy and to prevent selecting out-of-sample …

The recent success of supervised learning methods on ever larger offline datasets has
spurred interest in the reinforcement learning (RL) field to investigate whether the same …