object Policy
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 - Policy.scala
 
          
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        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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        constant[Obs, A, R, S[_]](a: A): Policy[Obs, A, R, Id, S]
      
      
      
Always return the same.
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        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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        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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        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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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.