Reward design is one of the most critical and challenging aspects when formulating a task
as a reinforcement learning (RL) problem. In practice, it often takes several attempts of …

Despite the recent success of representation learning in sequential decision making, the
study of the pure exploration scenario (ie, identify the best option and minimize the sample …

Despite rapid progress in theoretical reinforcement learning (RL) over the last few years,
most of the known guarantees are worst-case in nature, failing to take advantage of structure …

We study regret minimization for reinforcement learning (RL) in Latent Markov Decision
Processes (LMDPs) with context in hindsight. We design a novel model-based algorithmic …

Abstract Generalization in Reinforcement Learning (RL) aims to train an agent during
training that generalizes to the target environment. In this work, we first point out that RL …

Existing theoretical studies on offline reinforcement learning (RL) mostly consider a dataset
sampled directly from the target task. In practice, however, data often come from several …

We study the problem of online multi-task learning where the tasks are performed within
similar but not necessarily identical multi-armed bandit environments. In particular, we study …

In many real-world applications, collecting and labeling the data is expensive and time-
consuming. Thus, there is a need to obtain a high-performance learner by leveraging the …

The ability to leverage shared behaviors between tasks is critical for sample-efficient multi-
task reinforcement learning (MTRL). While prior methods have primarily explored parameter …

Multitask Reinforcement Learning (MTRL) approaches have gained increasing attention for
its wide applications in many important Reinforcement Learning (RL) tasks. However, while …