case class UCB[Obs, A, R, T, S[_]](config: Config[R, T], valueFn: ActionValueFn[Obs, A, Choice[T]], time: Time) extends Policy[Obs, A, R, Cat, S] with Product with Serializable
- Source
- UCB.scala
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Instance Constructors
- new UCB(config: Config[R, T], valueFn: ActionValueFn[Obs, A, Choice[T]], time: Time)
Value Members
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final
def
!=(arg0: Any): Boolean
- Definition Classes
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final
def
##(): Int
- Definition Classes
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final
def
==(arg0: Any): Boolean
- Definition Classes
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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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- protected[lang]
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- @throws( ... ) @native()
- 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
-
def
contramapReward[T](f: (T) ⇒ R)(implicit S: Functor[S]): Policy[Obs, A, T, Cat, S]
- Definition Classes
- Policy
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final
def
eq(arg0: AnyRef): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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final
def
isInstanceOf[T0]: Boolean
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def
learn(sars: SARS[Obs, A, R, S]): This
learn here passes directly through to the ActionValueFn now, which is the new thing.
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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.
- Definition Classes
- Policy
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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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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- val time: Time
- val valueFn: ActionValueFn[Obs, A, Choice[T]]
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final
def
wait(): Unit
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final
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.