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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Simulation-Based Algorithms for Markov Decision Processes

Simulation-Based Algorithms for Markov Decision Processes

Chang, Hyeong Soo
Hu, Jiaqiao
Fu, Michael C.

Hardcover

Series: Communications and Control Engineering

Business GeneralTechnology & EngineeringProbability & Statistics

ISBN10: 144715021X
ISBN13: 9781447150213
Publisher: Springer
Published: Mar 20 2013
Pages: 229
Weight: 1.40
Height: 0.70 Width: 6.30 Depth: 9.20
Language: English
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 dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search.

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