With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

While large-scale unsupervised language models (LMs) learn broad world knowledge and
some reasoning skills, achieving precise control of their behavior is difficult due to the …

Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that
improves over the behavior policy that collected the dataset, while at the same time …

Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously
collected static dataset, is an important paradigm of RL. Standard RL methods often perform …

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 …

Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data.
Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms …

Diffusion models are a class of flexible generative models trained with an approximation to
the log-likelihood objective. However, most use cases of diffusion models are not concerned …

In this work, we present a scalable reinforcement learning method for training multi-task
policies from large offline datasets that can leverage both human demonstrations and …

Reward and representation learning are two long-standing challenges for learning an
expanding set of robot manipulation skills from sensory observations. Given the inherent …