markov Decision Processes (mdps) Are A Mathematical Framework For Modeling Sequential Decision Problems Under Uncertainty As Well As Reinforcement Learning Problems. Written By Experts In The Field, This Book Provides A Global View Of Current Research Using Mdps In Artificial Intelligence. It Starts
Markov decision processes
β Scribed by D. J. White
- Book ID
- 127426103
- Publisher
- John Wiley & Sons
- Year
- 1993
- Tongue
- English
- Weight
- 1 MB
- Edition
- 1
- Category
- Library
- City
- Chichester [England]; New York
- ISBN-13
- 9780471936275
No coin nor oath required. For personal study only.
β¦ Synopsis
An up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. Concentrates on infinite-horizon discrete-time models. Discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models. Also covers modified policy iteration, multichain models with average reward criterion and sensitive optimality. Features a wealth of figures which illustrate examples and an extensive bibliography.
π SIMILAR VOLUMES
Examines several fundamentals concerning the manner in which Markov decision problems may be properly formulated and the determination of solutions or their properties. Coverage includes optimal equations, algorithms and their characteristics, probability distributions, modern development in the Mar
markov Decision Processes (mdps) Are A Mathematical Framework For Modeling Sequential Decision Problems Under Uncertainty As Well As Reinforcement Learning Problems. Written By Experts In The Field, This Book Provides A Global View Of Current Research Using Mdps In Artificial Intelligence. It Starts
markov Decision Processes (mdps) Are A Mathematical Framework For Modeling Sequential Decision Problems Under Uncertainty As Well As Reinforcement Learning Problems. Written By Experts In The Field, This Book Provides A Global View Of Current Research Using Mdps In Artificial Intelligence. It Starts