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Python Deep Learning - Second Edition
book

Python Deep Learning - Second Edition

by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
January 2019
Intermediate to advanced content levelIntermediate to advanced
386 pages
11h 13m
English
Packt Publishing
Content preview from Python Deep Learning - Second Edition

Exploring starts policy improvement

MC policy improvement follows the same general pattern as DP. That is, we alternate evaluation and improvement steps until convergence. But because we don't have a model of the environment, it is better to estimate the action-value function, (state-action pairs), instead of the state-value function. If we had a model, we could just follow a greedy policy and choose the combination of action/reward and next state value with the highest expected return (similar to DP). But here, the action values will be better for choosing new policy. Therefore, to find the optimal policy, we have to estimate . With MC, ...

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Publisher Resources

ISBN: 9781789348460Supplemental Content