Transfer in Reinforcement Learning Domains is popular PDF and ePub book, written by Matthew Taylor in 2009-05-19, it is a fantastic choice for those who relish reading online the Technology & Engineering genre. Let's immerse ourselves in this engaging Technology & Engineering book by exploring the summary and details provided below. Remember, Transfer in Reinforcement Learning Domains can be Read Online from any device for your convenience.

Transfer in Reinforcement Learning Domains Book PDF Summary

In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge. The key insight behind "transfer learning" is that generalization may occur not only within tasks, but also across tasks. While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research. The key contributions of this book are: Definition of the transfer problem in RL domains Background on RL, sufficient to allow a wide audience to understand discussed transfer concepts Taxonomy for transfer methods in RL Survey of existing approaches In-depth presentation of selected transfer methods Discussion of key open questions By way of the research presented in this book, the author has established himself as the pre-eminent worldwide expert on transfer learning in sequential decision making tasks. A particular strength of the research is its very thorough and methodical empirical evaluation, which Matthew presents, motivates, and analyzes clearly in prose throughout the book. Whether this is your initial introduction to the concept of transfer learning, or whether you are a practitioner in the field looking for nuanced details, I trust that you will find this book to be an enjoyable and enlightening read. Peter Stone, Associate Professor of Computer Science

Detail Book of Transfer in Reinforcement Learning Domains PDF

Transfer in Reinforcement Learning Domains
  • Author : Matthew Taylor
  • Release : 19 May 2009
  • Publisher : Springer
  • ISBN : 9783642018824
  • Genre : Technology & Engineering
  • Total Page : 237 pages
  • Language : English
  • PDF File Size : 16,9 Mb

If you're still pondering over how to secure a PDF or EPUB version of the book Transfer in Reinforcement Learning Domains by Matthew Taylor, don't worry! All you have to do is click the 'Get Book' buttons below to kick off your Download or Read Online journey. Just a friendly reminder: we don't upload or host the files ourselves.

Get Book

Federated and Transfer Learning

Federated and Transfer Learning Author : Roozbeh Razavi-Far,Boyu Wang,Matthew E. Taylor,Qiang Yang
Publisher : Springer Nature
File Size : 10,8 Mb
Get Book
This book provides a collection of recent research works on learning from decentralized data, transf...

Hands On Transfer Learning with Python

Hands On Transfer Learning with Python Author : Dipanjan Sarkar,Raghav Bali,Tamoghna Ghosh
Publisher : Packt Publishing Ltd
File Size : 43,6 Mb
Get Book
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next ...

Transfer Learning

Transfer Learning Author : Makoto Yamada,Jianhui Chen,Yi Chang
Publisher : Morgan Kaufmann
File Size : 29,7 Mb
Get Book
Transfer Learning: Algorithms and Applications presents an in-depth discussion on practices for tran...

Visual Domain Adaptation in the Deep Learning Era

Visual Domain Adaptation in the Deep Learning Era Author : Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Publisher : Springer Nature
File Size : 42,5 Mb
Get Book
Solving problems with deep neural networks typically relies on massive amounts of labeled training d...

Adversarial Machine Learning

Adversarial Machine Learning Author : Aneesh Sreevallabh Chivukula,Xinghao Yang,Bo Liu,Wei Liu,Wanlei Zhou
Publisher : Springer Nature
File Size : 32,9 Mb
Get Book
A critical challenge in deep learning is the vulnerability of deep learning networks to security att...