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

In recent years, the development of robotics and artificial intelligence (AI) systems has been
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …

Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for
training models with broad, general capabilities for text, images, and other modalities …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

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 …

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 …

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 …

Reinforcement learning promises to solve complex sequential-decision problems
autonomously by specifying a high-level reward function only. However, reinforcement …

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 …