Deep Reinforcement Learning (DRL) is considered a potential framework to improve many
real-world autonomous systems; it has attracted the attention of multiple and diverse fields …

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

Deep reinforcement learning (deep RL) research has grown significantly in recent years. A
number of software offerings now exist that provide stable, comprehensive implementations …

Various data augmentation techniques have been recently proposed in image-based deep
reinforcement learning (DRL). Although they empirically demonstrate the effectiveness of …

Deep reinforcement learning (RL) has achieved breakthrough results on many tasks, but
agents often fail to generalize beyond the environment they were trained in. As a result …

Abstract Deep Reinforcement Learning (DRL) represents a paradigm shift in artificial
intelligence, combining the strengths of neural networks with the decision-making process of …

Abstract Deep Reinforcement Learning (DRL) is an avenue of research in Artificial
Intelligence (AI) that has received increasing attention within the research community in …

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 …

Neural networks (NN) are gaining importance in sequential decision-making. Deep
reinforcement learning (DRL), in particular, is extremely successful in learning action …

In recent years the field of Reinforcement Learning has come across a series of
breakthroughs. By combining with developments from working with complex neural …