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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.  val allowedIterations: Long
5.  final def asInstanceOf[T0]: T0

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6.  def clone(): AnyRef

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7.  val emptyFn: StateValueFn[Position, DecayState[Double]]
8.  val epsilon: Double
9.  final def eq(arg0: AnyRef): Boolean

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10.  def equals(arg0: Any): Boolean

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11.  def figureFourOne(): Unit
12.  def finalize(): Unit

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13.  def fourOne(inPlace: Boolean): (StateValueFn[Position, DecayState[Double]], Long)
14.  def fourTwo(inPlace: Boolean): (StateValueFn[InvPair, DecayState[Double]], Config, Long)

    The big differences from the book version are:

    The big differences from the book version are:

    • Currently our Poisson distribution normalizes over the allowed values, rather than just truncating the chance of a value greater than the max to zero.
    • our Greedy policy randomly chooses from the entire greedy set, vs just choosing the "first" thing, like Numpy does.

    The Python version also keeps an actual greedy policy, which means that the policy starts by returning 0 no matter what, by design, instead of by acting as a random policy until it knows any better.

    Without that the generated values match.

    TODO ALSO... currently, the sweepUntil function only supports valueIteration or updating on every single sweep. The book actually wants to do a full round of policy evaluation before doing any policy improvement.

    We need to support that.

15.  val gamma: Double
16.  final def getClass(): Class[_]

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17.  val gridConf: Config
18.  def hashCode(): Int

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19.  final def isInstanceOf[T0]: Boolean

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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 runCarRental(): Unit

    I'm leaving this in a nightmare state for now.

    I'm leaving this in a nightmare state for now. To finish this out, we really need to:

    • add support for policy evaluation and policy stability checks, alternating.
    • come up with some way of actually turning a particular policy's decisions into a heat map that's not so hardcoded
    • NOT have the graph library explode when I cancel a run, for Heatmap.
25.  def shouldStop[Obs, T](l: StateValueFn[Obs, T], r: StateValueFn[Obs, T], iterations: Long, verbose: Boolean = false)(implicit arg0: ToDouble[T]): Boolean
26.  final def synchronized[T0](arg0: ⇒ T0): T0

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27.  def toString(): String

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28.  def vfToSeqPoints(vf: StateValueFn[InvPair, DecayState[Double]]): Seq[Seq[Double]]

    This currently is not great because we don't have a way of automatically binning the data and generating that graph.

    This currently is not great because we don't have a way of automatically binning the data and generating that graph. This is custom.

29.  final def wait(): Unit

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

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

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