September 2018
Intermediate to advanced
288 pages
7h 38m
English
book
Keras Reinforcement Learning Projects
by Giuseppe Ciaburro, Sudharsan Ravichandiran, Suriyadeepan Ramamoorthy
Content preview from Keras Reinforcement Learning Projects
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Markov chains
A Markov chain is a mathematical model of a random phenomenon that evolves over time in such a way that the past influences the future only through the present. The time can be discrete (a whole variable), continuous (a real variable), or, more generally, a totally ordered whole. In this discussion, only discrete chains are considered. Markov chains were introduced in 1906 by Andrei Andreyevich Markov (1856–1922), from whom the name derives.
The example of a one-dimensional random walk seen in the previous section is a Markov chain; the next value in the chain is a unit that is more or less than the current value with the same probability of occurrence, regardless of the way in which the current value was reached.
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