Reinforcement Learning and Dynamic Programming Using Function Approximators Download

Author: Lucian Busoniu
Edition: 1
Binding: Hardcover
ISBN: 1439821089

Reinforcement Learning and Dynamic Programming Using Function Approximators (Automation and Control Engineering)

From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. Download Reinforcement Learning and Dynamic Programming Using Function Approximators (Automation and Control Engineering) from rapidshare, mediafire, 4shared. 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. AHowever, 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 Search and find a lot of engineering books in many category availabe for free download.

Download Reinforcement Learning and Dynamic Programming Using Function Approximators

Download Reinforcement Learning and Dynamic Programming Using Function Approximators engineering books for free. 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 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

Other engineering books

Reinforcement Learning: State-of-the-Art (Adaptation, Learning, and Optimization)

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior

Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective.What distinguishes reinforcement learning from supervised learning is t

Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain env

Approximate Dynamic Programming: Solving the Curses of Dimensionality, 2nd Edition (Wiley Series in Probability and Statistics)

Praise for the First Edition"Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners."
-C

Dynamic Programming and Optimal Control (2 Vol Set)

A two-volume set, consisting of the latest editions of the two volumes (3rd edition (2005) for Vol. I, and 4th edition (2012) for Vol. II). Much supplementary material can be found at the book's web page. The first volume is oriented towards modeling