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trait Policy[Obs, A, R, M[_], S[_]] extends AnyRef

This is how agents actually choose what comes next. This is a stochastic policy. We have to to be able to match this up with a state that has the same monadic return type, but for now it's hardcoded.

A - Action Obs - the observation offered by this state. R - reward M - the monadic type offered by the policy. S - the monad for the state.

Self Type
Policy[Obs, A, R, M, S]
Source
Policy.scala
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Type Members

  1. type This = Policy[Obs, A, R, M, S]

Abstract Value Members

  1. abstract def choose(state: State[Obs, A, R, S]): M[A]

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[lang]
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    @throws( ... ) @native()
  6. def contramapObservation[P](f: (P) ⇒ Obs)(implicit S: Functor[S]): Policy[P, A, R, M, S]
  7. def contramapReward[T](f: (T) ⇒ R)(implicit S: Functor[S]): Policy[Obs, A, T, M, S]
  8. final def eq(arg0: AnyRef): Boolean
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  9. def equals(arg0: Any): Boolean
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  10. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]
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  12. def hashCode(): Int
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    @native()
  13. final def isInstanceOf[T0]: Boolean
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  14. def learn(sars: SARS[Obs, A, R, S]): This
  15. def mapK[N[_]](f: FunctionK[M, 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.

  16. final def ne(arg0: AnyRef): Boolean
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  17. final def notify(): Unit
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  18. final def notifyAll(): Unit
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  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  20. def toString(): String
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  21. final def wait(): Unit
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  22. final def wait(arg0: Long, arg1: Int): Unit
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  23. final def wait(arg0: Long): Unit
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