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

Decisions made by mammals and birds are often temporally extended. They require
planning and sampling of decision-relevant information. Our understanding of such decision …

We introduce an exploration bonus for deep reinforcement learning methods that is easy to
implement and adds minimal overhead to the computation performed. The bonus is the error …

We introduce a new unsupervised pre-training method for reinforcement learning called
APT, which stands for Active Pre-Training. APT learns behaviors and representations by …

We introduce a new unsupervised pretraining objective for reinforcement learning. During
the unsupervised reward-free pretraining phase, the agent maximizes mutual information …

Due to its limited intelligence and abilities, machine learning is currently unable to handle
various situations thus cannot completely replace humans in real-world applications …

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

We present BRIEE, an algorithm for efficient reinforcement learning in Markov Decision
Processes with block-structured dynamics (ie, Block MDPs), where rich observations are …

Efficient exploration under sparse rewards remains a key challenge in deep reinforcement
learning. Previous exploration methods (eg, RND) have achieved strong results in multiple …

In recent years, a number of reinforcement learning (RL) methods have been pro-posed to
explore complex environments which differ across episodes. In this work, we show that the …