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.  def diffBelow[Obs, T](l: StateValueFn[Obs, T], r: StateValueFn[Obs, T], epsilon: Double)(combine: (Double, Double) ⇒ Double)(implicit arg0: ToDouble[T]): Boolean

   Helper to tell if we can stop iterating.

   Helper to tell if we can stop iterating. The combine function is used to aggregate the differences between the value functions for each observation... the final aggregated value must be less than epsilon to return true, false otherwise.

7.  def diffValue[Obs, T](l: StateValueFn[Obs, T], r: StateValueFn[Obs, T], combine: (Double, Double) ⇒ Double)(implicit T: ToDouble[T]): Double

   TODO consider putting this on the actual trait.

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

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9.  def equals(arg0: Any): Boolean

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10.  def finalize(): Unit

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11.  final def getClass(): Class[_]

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    @native()

12.  def hashCode(): Int

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13.  final def isInstanceOf[T0]: Boolean

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14.  def isPolicyStable[Obs, A, R, T, M[_], S[_]](l: StateValueFn[Obs, T], r: StateValueFn[Obs, T], prepare: (R) ⇒ T, merge: (T, T) ⇒ T, states: Traversable[State[Obs, A, R, S]])(implicit arg0: DModule[T], arg1: Ordering[T], arg2: Expectation[S]): Boolean
15.  final def ne(arg0: AnyRef): Boolean

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16.  final def notify(): Unit

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17.  final def notifyAll(): Unit

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18.  def sweep[Obs, A, R, T, M[_], S[_]](valueFn: StateValueFn[Obs, T], policyFn: (StateValueFn[Obs, T]) ⇒ Policy[Obs, A, R, M, S], evaluatorFn: (StateValueFn[Obs, T], Policy[Obs, A, R, M, S]) ⇒ StateValue[Obs, A, R, T, S], states: Traversable[State[Obs, A, R, S]], inPlace: Boolean, valueIteration: Boolean)(implicit arg0: Expectation[M], arg1: Expectation[S]): StateValueFn[Obs, T]

    This sweeps across the whole state space and updates the policy every single time IF you set valueIteration to true.

    This sweeps across the whole state space and updates the policy every single time IF you set valueIteration to true. Otherwise it creates a policy once and then uses it each time.

    What we really want is the ability to ping between updates to the value function or learning steps; to insert them every so often.

    This function does NOT currently return the final policy, since you can just make it yourself, given the return value and the function.

19.  def sweepUntil[Obs, A, R, T, M[_], S[_]](valueFn: StateValueFn[Obs, T], policyFn: (StateValueFn[Obs, T]) ⇒ Policy[Obs, A, R, M, S], evaluatorFn: (StateValueFn[Obs, T], Policy[Obs, A, R, M, S]) ⇒ StateValue[Obs, A, R, T, S], states: Traversable[State[Obs, A, R, S]], stopFn: (StateValueFn[Obs, T], StateValueFn[Obs, T], Long) ⇒ Boolean, inPlace: Boolean, valueIteration: Boolean)(implicit arg0: Expectation[M], arg1: Expectation[S]): (StateValueFn[Obs, T], Long)
20.  final def synchronized[T0](arg0: ⇒ T0): T0

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21.  def toString(): String

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22.  final def wait(): Unit

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

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

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25.  object Update extends Serializable