| #1654682 in Books | 2016-09-10 | 2016-09-10 | Original language:English | PDF # 1 | 9.25 x1.21 x6.10l,1.64 | File type: PDF | 508 pages||1 of 1 people found the following review helpful.| The students loved it. It was comprehensive|By CG|I recently used this to teach a graduate course taken by industrial engineering and mechanical engineering students. The students loved it. It was comprehensive, yet easy to understand the theory behind topics such as neural networks and reinforcement learning, 2 commonly used data and decision analytic techniques.|1 of 1 peop|From the Back Cover||Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.
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You can specify the type of files you want, for your device.Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series) | Abhijit Gosavi. Which are the reasons I like to read books. Great story by a great author.
