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

Reward-free reinforcement learning (RL) considers the setting where the agent does not
have access to a reward function during exploration, but must propose a near-optimal policy …

We study time-inhomogeneous episodic reinforcement learning (RL) under general function
approximation and sparse rewards. We design a new algorithm, Variance-weighted …

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 …

Recent studies have shown that episodic reinforcement learning (RL) is not more difficult
than bandits, even with a long planning horizon and unknown state transitions. However …

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 …

This paper gives the first polynomial-time algorithm for tabular Markov Decision Processes
(MDP) that enjoys a regret bound\emph {independent on the planning horizon}. Specifically …

We study the problem of reinforcement learning (RL) with low (policy) switching cost {—} a
problem well-motivated by real-life RL applications in which deployments of new policies …

The theory of reinforcement learning has focused on two fundamental problems: achieving
low regret, and identifying $\epsilon $-optimal policies. While a simple reduction allows one …