<p>Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of d
Simulation-Based Algorithms For Markov Decision Processes
โ Scribed by Hyeong Soo Chang, Michael C. Fu, Jiaqiao Hu, Steven I. Marcus
- Publisher
- Springer
- Year
- 2007
- Tongue
- English
- Leaves
- 202
- Series
- Communications and control engineering
- Category
- Library
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๐ SIMILAR VOLUMES
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. It is well-known that many real-world problems modeled by MDPs have huge state and/or action spaces, leading to the n
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. This book brings the state-of-the-art research together for the first time. It provides practical modeling methods fo
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