July 2017
Beginner to intermediate
442 pages
10h 8m
English
Content preview from Statistics for Machine Learning
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Dynamic programming
Dynamic programming is a sequential way of solving complex problems by breaking them down into sub-problems and solving each of them. Once it solves the sub-problems, then it puts those subproblem solutions together to solve the original complex problem. In the reinforcement learning world, Dynamic Programming is a solution methodology to compute optimal policies given a perfect model of the environment as a Markov Decision Process (MDP).
Dynamic programming holds good for problems which have the following two properties. MDPs in fact satisfy both properties, which makes DP a good fit for solving them by solving Bellman Equations:
- Optimal substructure
- Principle of optimality applies
- Optimal solution can be decomposed ...
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