Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

The way people travel, organise their time, and acquire information has changed due to
information technologies. Artificial intelligence (AI) and machine learning (ML) are …

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

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 …

Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

In recent years, neural networks have enjoyed a renaissance as function approximators in
reinforcement learning. Two decades after Tesauro's TD-Gammon achieved near top-level …

Abstract Bellemare et al.(2016) introduced the notion of a pseudo-count, derived from a
density model, to generalize count-based exploration to non-tabular reinforcement learning …