Value Members

1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

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6.  final def eq(arg0: AnyRef): Boolean

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7.  def equals(arg0: Any): Boolean

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8.  implicit val evaluator: Numeric[Real]
9.  def figureFiveFour(): Unit

   this checks using the random policy to check the stickHigh behavior policy, and compares ordinary and weighted off-policy sampling.

10.  def figureFiveOne(): Unit

    This is the figure that explores the stickHigh strategy over a bunch of states, tracking what happens with a usable ace and with no usable ace.

11.  def figureFiveThree(): Unit

    this checks using the random policy to check the stickHigh behavior policy, and compares ordinary and weighted off-policy sampling.

12.  def figureFiveTwo(): Unit

    This uses exploring starts to capture the optimal policy.

    This uses exploring starts to capture the optimal policy.

    • go through a single round of the game, then
    • update the policy to use the new function.

    the policy gets updated on every play at the end of the trajectory walk;

13.  def finalize(): Unit

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14.  final def getClass(): Class[_]

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15.  def hashCode(): Int

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16.  def importanceSampling(): Unit
17.  final def isInstanceOf[T0]: Boolean

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18.  val limited: Generator[State[AgentView, Action, Double, Generator]]
19.  def limitedM[M[_]](state: M[Blackjack[M]])(implicit arg0: Functor[M]): M[State[AgentView, Action, Double, M]]

    Is this appreciably slower? This is going to be useful, in any case, when I'm working with the tests.

20.  def main(items: Array[String]): Unit
21.  final def ne(arg0: AnyRef): Boolean

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22.  final def notify(): Unit

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23.  final def notifyAll(): Unit

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24.  def random[M[_]]: Policy[AgentView, Action, Double, Cat, M]
25.  implicit val rng: RNG
26.  val starter: Generator[Blackjack[Generator]]
27.  def stickHigh[S[_]](hitBelow: Int): Policy[AgentView, Action, Double, Id, S]

    Simple blackjack policy for the demos below.

28.  def stickHighCat[S[_]](hitBelow: Int): Policy[AgentView, Action, Double, Cat, S]
29.  final def synchronized[T0](arg0: ⇒ T0): T0

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30.  def toString(): String

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31.  val uniformStarts: Generator[Blackjack[Generator]]
32.  def updateFn[Obs, A, R, G, M[_]](g: M[State[Obs, A, R, M]], agg: MonoidAggregator[SARS[Obs, A, R, M], G, Option[G]], policyFn: (ActionValueFn[Obs, A, G]) ⇒ Policy[Obs, A, R, M, M])(implicit arg0: Monad[M]): Loop[M, ActionValueFn[Obs, A, G]]

    Obs, A, R, M make sense here.

    Obs, A, R, M make sense here. They have to line up with the state. So what is T? T is the type that you use to walk back along the trajectory.

    If you have NO decay you want to supply a Double.

    If you decay you need to supply a DecayState.

    Then, internal to the value function, is the aggregation.

33.  final def wait(): Unit

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34.  final def wait(arg0: Long, arg1: Int): Unit

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35.  final def wait(arg0: Long): Unit

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