Foundation models have shown impressive adaptation and scalability in supervised and self-
supervised learning problems, but so far these successes have not fully translated to …

Foundation models have shown impressive adaptation and scalability in supervised and self-
supervised learning problems, but so far these successes have not fully translated to …

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

The main challenge in vision-and-language navigation (VLN) is how to understand natural-
language instructions in an unseen environment. The main limitation of conventional VLN …

Unsupervised reinforcement learning aims at learning a generalist policy in a reward-free
manner for fast adaptation to downstream tasks. Most of the existing methods propose to …

Dropped into an unknown environment, what should an agent do to quickly learn about the
environment and how to accomplish diverse tasks within it? We address this question within …

We identify and study the phenomenon of policy churn, that is, the rapid change of the
greedy policy in value-based reinforcement learning. Policy churn operates at a surprisingly …

An agent trained within a closed system can master any desired capability, as long as the
following three conditions hold:(a) it receives sufficiently informative and aligned …

Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous
movements despite their vast amount of muscles. Reinforcement learning (RL) on large …

Existing multiagent exploration works focus on how to explore in the fully cooperative task,
which is insufficient in the environment with nonstationarity induced by agent interactions. To …