In artificial intelligence the aim is to build intelligent entities (Russell and Norvig, 2009).
According to some, an artificial entity can be called intelligent when it can successfully mimic …

The reinforcement learning problem is the challenge of AI in a microcosm; how can we build
an agent that can plan, learn, perceive, and act in a complex world? There's a great new …

Reinforcement learning is the learning of a mapping from situations to actions so as to
maximize a scalar reward or reinforcement signal. The learner is not told which action to …

Five relatively recent applications of reinforcement learning methods are described. These
examples were chosen to illustrate a diversity of application types, the engineering needed …

Reinforcement learning (RL) concerns the problem of a learning agent inter-acting with its
environment to achieve a goal. Instead of being given examples of desired behavior, the …

Reinforcement learning (RL) is a set of mathematical methods and algorithms that can be
applied to a wide array of problems and plays a central role in machine learning. The aim of …

This book grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in
the winter semester 2018/2019 at Technische Universität Darmstadt. Student research …

Reinforcement learning (RL) is learning by interacting with an environment. An RL agent
learns from the consequences of its actions, rather than from being explicitly taught and it …

This article is related to the research effort of constructing an intelligent agent, ie, a computer
system that is able to sense its environment (world), reason utilizing its internal knowledge …

Suppose we want to use an intelligent agent (computer program or robot) for performing
tasks for us, but we cannot or do not want to specify the precise task-operations. Eg we may …