Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step
towards building autonomous systems with a higher level understanding of the visual world …

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 …

A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

Recently, researchers have made significant progress combining the advances in deep
learning for learning feature representations with reinforcement learning. Some notable …

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

We study the reachability problem for systems implemented as feed-forward neural networks
whose activation function is implemented via ReLU functions. We draw a correspondence …

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 …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

The significantly expanded and updated new edition of a widely used text on reinforcement
learning, one of the most active research areas in artificial intelligence. Reinforcement …