While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

We present BRIEE, an algorithm for efficient reinforcement learning in Markov Decision
Processes with block-structured dynamics (ie, Block MDPs), where rich observations are …

Thompson sampling and other Bayesian sequential decision-making algorithms are among
the most popular approaches to tackle explore/exploit trade-offs in (contextual) bandits. The …

Efficient exploration in bandits is a fundamental online learning problem. We propose a
variant of Thompson sampling that learns to explore better as it interacts with bandit …

We study the problem of representation transfer in offline Reinforcement Learning (RL),
where a learner has access to episodic data from a number of source tasks collected a …

We study the problem of representational transfer in RL, where an agent first pretrains in a
number of\emph {source tasks} to discover a shared representation, which is subsequently …

Abstract Meta-, multi-task, and federated learning can be all viewed as solving similar tasks,
drawn from a distribution that reflects task similarities. We provide a unified view of all these …

As representation learning becomes a powerful technique to reduce sample complexity in
reinforcement learning (RL) in practice, theoretical understanding of its advantage is still …

This survey is focused on certain sequential decision-making problems that involve
optimizing over probability functions. We discuss the relevance of these problems for …

Abstract We propose AdaTS, a Thompson sampling algorithm that adapts sequentially to
bandit tasks that it interacts with. The key idea in AdaTS is to adapt to an unknown task prior …