object DecayState extends Serializable
- Source
- DecayState.scala
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Type Members
- case class DecayedValue[A](get: A) extends DecayState[A] with Product with Serializable
- case class Reward[A](get: A) extends DecayState[A] with Product with Serializable
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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def
bellmanFn[Obs, A, R, T, M[_], S[_]](gamma: Double)(implicit arg0: DModule[R], arg1: Expectation[M], arg2: Expectation[S]): (StateValueFn[Obs, DecayState[R]], Policy[Obs, A, R, M, S]) ⇒ StateValue[Obs, A, R, DecayState[R], S]
Filling in.
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def
clone(): AnyRef
- Attributes
- protected[lang]
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- @throws( ... ) @native()
- def decayStateGroup[A](gamma: Double)(implicit M: Module[Double, A]): Group[DecayState[A]]
- def decayStateModule[A](gamma: Double)(implicit M: Module[Double, A]): Module[Double, DecayState[A]]
- def decayStateVectorSpace[A](gamma: Double)(implicit arg0: Ring[A], M: Module[Double, A]): VectorSpace[A, DecayState]
- implicit def dsOrd[A](implicit arg0: Ordering[A]): Ordering[DecayState[A]]
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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- implicit def toDouble[A](implicit A: ToDouble[A]): ToDouble[DecayState[A]]
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def
toString(): String
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final
def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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ScalaRL
This is the API documentation for the ScalaRL functional reinforcement learning library.
Further documentation for ScalaRL can be found at the documentation site.
Check out the ScalaRL package list for all the goods.