We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven
exploration in visually complex environments. BYOL-Explore learns the world …

The low-rank MDP has emerged as an important model for studying representation learning
and exploration in reinforcement learning. With a known representation, several model-free …

Realistic environments often provide agents with very limited feedback. When the
environment is initially unknown, the feedback, in the beginning, can be completely absent …

Maximising a cumulative reward function that is Markov and stationary, ie, defined over state-
action pairs and independent of time, is sufficient to capture many kinds of goals in a Markov …

Finding unified complexity measures and algorithms for sample-efficient learning is a central
topic of research in reinforcement learning (RL). The Decision-Estimation Coefficient (DEC) …

In some applications of reinforcement learning, a dataset of pre-collected experience is
already availablebut it is also possible to acquire some additional online data to help …

We study reward-free reinforcement learning (RL) under general non-linear function
approximation, and establish sample efficiency and hardness results under various standard …

Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked
processes of reinforcement learning: collecting informative experience and inferring optimal …

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite
its progress, current algorithms are still unsatisfactory in virtually every aspect of the …

There has been growing progress on theoretical analyses for provably efficient learning in
MDPs with linear function approximation, but much of the existing work has made strong …