A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
Language: en
Pages: 76
Authors: Alborz Geramifard
Categories: Markov processes
Type: BOOK - Published: 2013 - Publisher:

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A Markov Decision Process (MDP) is a natural framework for formulating sequential decision-making problems under uncertainty. In recent years, researchers have
Reinforcement Learning and Dynamic Programming Using Function Approximators
Language: en
Pages: 280
Authors: Lucian Busoniu
Categories: Computers
Type: BOOK - Published: 2017-07-28 - Publisher: CRC Press

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From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control the
Reinforcement Learning and Dynamic Programming Using Function Approximators
Language: en
Pages: 270
Authors:
Categories: Digital control systems
Type: BOOK - Published: 2010 - Publisher:

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Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy
Automatic Basis Function Construction for Reinforcement Learning and Approximate Dynamic Programming
Language: en
Pages: 86
Authors: Philipp W. Keller
Categories: Dynamic programming
Type: BOOK - Published: 2008 - Publisher:

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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
Language: en
Pages: 92
Authors: Alborz Geramifard
Categories: Computers
Type: BOOK - Published: 2013-12 - Publisher:

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This tutorial reviews techniques for planning and learning in Markov Decision Processes (MDPs) with linear function approximation of the value function. Two maj