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

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 …

We address the challenge of exploration in reinforcement learning (RL) when the agent
operates in an unknown environment with sparse or no rewards. In this work, we study the …

Reinforcement learning algorithms commonly seek to optimize policies for solving one
particular task. How should we explore an unknown dynamical system such that the …

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 …

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

Collecting and leveraging data with good coverage properties plays a crucial role in different
aspects of reinforcement learning (RL), including reward-free exploration and offline …

In classic Reinforcement Learning (RL), the agent maximizes an additive objective of the
visited states, eg, a value function. Unfortunately, objectives of this type cannot model many …

We consider the problem of model estimation in episodic Block MDPs. In these MDPs, the
decision maker has access to rich observations or contexts generated from a small number …

In the sequential decision making setting, an agent aims to achieve systematic
generalization over a large, possibly infinite, set of environments. Such environments are …