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 …

Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

According to cognitive psychology and related disciplines, the development of complex
problem-solving behaviour in biological agents depends on hierarchical cognitive …

Goals play a central role in human cognition. However, computational theories of learning
and decision-making often take goals as given. Here, we review key empirical findings …

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

Planning methods can solve temporally extended sequential decision making problems by
composing simple behaviors. However, planning requires suitable abstractions for the states …

Recent exploration methods have proven to be a recipe for improving sample-efficiency in
deep reinforcement learning (RL). However, efficient exploration in high-dimensional …

Effective exploration is a challenge in reinforcement learning (RL). Novelty-based
exploration methods can suffer in high-dimensional state spaces, such as continuous …

Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in
Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of …