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

Exploration is widely regarded as one of the most challenging aspects of reinforcement
learning (RL), with many naive approaches succumbing to exponential sample complexity …

Convex Reinforcement Learning (RL) is a recently introduced framework that generalizes
the standard RL objective to any convex (or concave) function of the state distribution …

Animals have a developed ability to explore that aids them in important tasks such as
locating food, exploring for shelter, and finding misplaced items. These exploration skills …

In the absence of assigned tasks, a learning agent typically seeks to explore its environment
efficiently. However, the pursuit of exploration will bring more safety risks. An under-explored …

We propose an algorithm for tabular episodic reinforcement learning with constraints. We
provide a modular analysis with strong theoretical guarantees for settings with concave …

In reinforcement learning (RL), rewards of states are typically considered additive, and
following the Markov assumption, they are $\textit {independent} $ of states visited …

We consider the problem of constrained Markov Decision Process (CMDP) where an agent
interacts with an ergodic Markov Decision Process. At every interaction, the agent obtains a …

A key challenge in science and engineering is to design experiments to learn about some
unknown quantity of interest. Classical experimental design optimally allocates the …

In the maximum state entropy exploration framework, an agent interacts with a reward-free
environment to learn a policy that maximizes the entropy of the expected state visitations it is …