object Policy
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- Policy.scala
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def
!=(arg0: Any): Boolean
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asInstanceOf[T0]: T0
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def
choose[Obs, A, R, M[_], S[_]](chooseFn: (State[Obs, A, R, S]) ⇒ M[A]): Policy[Obs, A, R, M, S]
If all you care about is a choose fn.
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def
clone(): AnyRef
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def
constant[Obs, A, R, S[_]](a: A): Policy[Obs, A, R, Id, S]
Always return the same.
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def
epsilonGreedy[Obs, A, R, T, S[_]](evaluator: ActionValue[Obs, A, R, T, S], epsilon: Double)(implicit arg0: Ordering[T]): Greedy[Obs, A, R, T, S]
In between.
In between. This is equal to
epsilonGreedy(evaluator, 1.0) == greedy(evaluator).mapK(Cat.setToCat) epsilonGreedy(evaluator, 0.0) == random.mapK(Cat.setToCat)
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eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
greedy[Obs, A, R, T, S[_]](evaluator: ActionValue[Obs, A, R, T, S])(implicit arg0: Ordering[T]): Policy[Obs, A, R, Cat, S]
Full greed.
Full greed. mapK(Categorical.setToCat) to get the usual Greedy.
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hashCode(): Int
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isInstanceOf[T0]: Boolean
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ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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final
def
notifyAll(): Unit
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def
random[Obs, A, R, S[_]]: Policy[Obs, A, R, Cat, S]
Full exploration.
Full exploration. mapK(Categorical.setToCat) to get the usual Greedy.
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
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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.