In recent years, significant progress has been made in solving challenging problems across
various domains using deep reinforcement learning (RL). Reproducing existing work and …

The success of deep learning in the computer vision and natural language processing
communities can be attributed to training of very deep neural networks with millions or …

We propose a novel training algorithm for reinforcement learning which combines the
strength of deep Q-learning with a constrained optimization approach to tighten optimality …

The offline reinforcement learning (RL) setting (also known as full batch RL), where a policy
is learned from a static dataset, is compelling as progress enables RL methods to take …

In recent years there have been many successes of using deep representations in
reinforcement learning. Still, many of these applications use conventional architectures …

CleanRL is an open-source library that provides high-quality single-file implementations of
Deep Reinforcement Learning (DRL) algorithms. These single-file implementations are self …

In this chapter, we introduce the fundamentals of classical reinforcement learning and
provide a general overview of deep reinforcement learning. We first start with the basic …

Deep reinforcement learning has gathered much attention recently. Impressive results were
achieved in activities as diverse as autonomous driving, game playing, molecular …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …