July 2017
Beginner to intermediate
442 pages
10h 8m
English
Content preview from Statistics for Machine Learning
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Comparison between dynamic programming and Monte Carlo methods
Dynamic programming requires a complete knowledge of the environment or all possible transitions, whereas Monte Carlo methods work on a sampled state-action trajectory on one episode. DP includes only one-step transition, whereas MC goes all the way to the end of the episode to the terminal node. One important fact about the MC method is that the estimates for each state are independent, which means the estimate for one state does not build upon the estimate of any other state, as in the case of DP.
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