Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Networking with cognitive packets

This paper discusses a novel packet computer network architecture, a Cognitive Packet Network (CPN) ; in which intelligent capabilities for routing and flow control are moved towards the packets, rather than being concentrated in the nodes. The routing algorithm in CPN uses reinforcement learning based on the Random Neural Network. We outline the design of CPN and show how it incorporates packet loss and delay directly into user Quality of Service (QoS) criteria, and use these criteria to conduct routing. We then present our experimental test-bed and report on extensive measurement experiments. These experiments include measurements of the network under link and node failures. They illustrate the manner in which neural network based CPN can be used to support a reliable adaptive network environment for peer-to-peer communications over an unreliable infrastructure