A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks.
Recent progress in text-guided image synthesis has yielded models with an impressive …

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 …

In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …

We study model-based offline Reinforcement Learning with general function approximation
without a full coverage assumption on the offline data distribution. We present an algorithm …

Two central paradigms have emerged in the reinforcement learning (RL) community: online
RL and offline RL. In the online RL setting, the agent has no prior knowledge of the …

We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has
access to an offline dataset and the ability to collect experience via real-world online …

A major challenge in reinforcement learning is to develop practical, sample-efficient
algorithms for exploration in high-dimensional domains where generalization and function …

We study the design of sample-efficient algorithms for reinforcement learning in the
presence of rich, high-dimensional observations, formalized via the Block MDP problem …

In view of its power in extracting feature representation, contrastive self-supervised learning
has been successfully integrated into the practice of (deep) reinforcement learning (RL) …

Representation learning often plays a critical role in reinforcement learning by managing the
curse of dimensionality. A representative class of algorithms exploits a spectral …