This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Passive observational data, such as human videos, is abundant and rich in information, yet
remains largely untapped by current RL methods. Perhaps surprisingly, we show that …

Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …

The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a
notoriously difficult problem domain for the application of neural networks. We identify a …

In this paper we introduce a simple approach for exploration in reinforcement learning (RL)
that allows us to develop theoretically justified algorithms in the tabular case but that is also …

We study the learning dynamics of self-predictive learning for reinforcement learning, a
family of algorithms that learn representations by minimizing the prediction error of their own …

Memory and planning rely on learning the structure of relationships among experiences.
Compact representations of these structures guide flexible behavior in humans and animals …

Deep reinforcement learning (DRL) has gained great success by learning directly from high-
dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of …

Autonomously discovering temporally extended actions, or skills, is a longstanding goal of
hierarchical reinforcement learning. We propose a new algorithm that combines skill …