Reinforcement Learning and Dynamic Programming Using Function Approximators - ISBN:1439821089

Reinforcement Learning and Dynamic Programming Using Function Approximators

Date: 15 April 2011, 04:43

Free register and download UseNet downloader, then you can FREE Download from UseNet.

Download without Limit " Reinforcement Learning and Dynamic Programming Using Function Approximators " from UseNet for FREE!

This book provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade.
From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems.
However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence.

Download Links:

Download Link (High Speed Download)

Download Link 1 (http://uploading.com/...1439)

Download Link 2 (http://depositfiles.com/...b3g8xuuoz)

DISCLAIMER:

This site does not store Reinforcement Learning and Dynamic Programming Using Function Approximators on its server. We only index and link to Reinforcement Learning and Dynamic Programming Using Function Approximators provided by other sites. Please contact the content providers to delete Reinforcement Learning and Dynamic Programming Using Function Approximators if any and email us, we'll remove relevant links or contents immediately.