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

Deep reinforcement learning (RL) has become one of the most popular topics in artificial
intelligence research. It has been widely used in various fields, such as end-to-end control …

Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

Atari games have been a long-standing benchmark in the reinforcement learning (RL)
community for the past decade. This benchmark was proposed to test general competency …

We introduce a new generative model where samples are produced via Langevin dynamics
using gradients of the data distribution estimated with score matching. Because gradients …

Through multi-agent competition, the simple objective of hide-and-seek, and standard
reinforcement learning algorithms at scale, we find that agents create a self-supervised …

Recent unsupervised pre-training methods have shown to be effective on language and
vision domains by learning useful representations for multiple downstream tasks. In this …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

Reinforcement learning allows solving complex tasks, however, the learning tends to be task-
specific and the sample efficiency remains a challenge. We present Plan2Explore, a self …

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 …