case class Greedy[Obs, A, R, T, S[_]](config: Config[R, T], valueFn: ActionValueFn[Obs, A, T])(implicit evidence$1: Ordering[T]) extends Policy[Obs, A, R, Cat, S] with Product with Serializable
- Source
- Greedy.scala
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Instance Constructors
- new Greedy(config: Config[R, T], valueFn: ActionValueFn[Obs, A, T])(implicit arg0: Ordering[T])
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 choose(state: State[Obs, A, R, S]): Cat[A]
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def
clone(): AnyRef
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- val config: Config[R, T]
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def
contramapObservation[P](f: (P) ⇒ Obs)(implicit S: Functor[S]): Policy[P, A, R, Cat, S]
- Definition Classes
- Policy
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def
contramapReward[T](f: (T) ⇒ R)(implicit S: Functor[S]): Policy[Obs, A, T, Cat, S]
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final
def
eq(arg0: AnyRef): Boolean
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def
finalize(): Unit
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def
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final
def
isInstanceOf[T0]: Boolean
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- def learn(sars: SARS[Obs, A, R, S]): This
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def
mapK[N[_]](f: FunctionK[Cat, N]): Policy[Obs, A, R, N, S]
Just an idea to see if I can make stochastic deciders out of deterministic deciders.
Just an idea to see if I can make stochastic deciders out of deterministic deciders. We'll see how this develops.
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final
def
ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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def
notifyAll(): Unit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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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.