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I know that v10 has support for both Machine Learning and Finite Markov Processes, the Q-learning algorithm uses both. Specifically, Q-learning finds an optimal action-selection policy for any given (finite) Markov decision process (MDP).

Are there any specific combinations of functions and options that I should use as a starting point? or should I do this from scratch?

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Right now you can use MDPtoolbox via RLink.

Reinforcement learning is expected in the next version of Mathematica. You can see this presentation from the Wolfram Conference 2015. A lot of interesting things are expected in the machine learning functionality (slides 10, 11 or images below).

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    $\begingroup$ Could you please give a minimal work code example using nettrains? $\endgroup$ – M.R. Feb 25 at 8:16
  • $\begingroup$ @M.R. link1 and link2 $\endgroup$ – Alexey Golyshev Feb 25 at 12:51

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