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Chapter2

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# Introduction to Chapter 2

This chapter is about Bandits. These are markov processes that know about a single state, really. The trick here is going to be getting the stuff that plays these particular states to be more general, and work with the same machinery that rolls states forward.

What we REALLY NEED here is both the top and bottom graphs, getting it done.

The top graph is the average reward across GAMES per step.

So we really want to march them ALL forward and grab the average reward...

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Chapter2.scala
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  17. def nonStationaryTestbed(nArms: Int, mean: Double, stdDev: Double): Generator[State[Unit, Arm, Double, Generator]]

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  21. def playBandit[Obs, A, R](policy: Policy[Obs, A, R, Generator, Generator], stateGen: Generator[State[Obs, A, R, Generator]], nRuns: Int, timeSteps: Int)(reduce: (List[SARS[Obs, A, R, Generator]]) ⇒ R): (List[Moment[Obs, A, R, Generator]], List[R])
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