Reinforcement Learning and Dynamic Programming Using Function Approximators

Author: Lucian Busoniu
Edition: 1
Binding: Kindle Edition
ISBN: B00918SK90

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. 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 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

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