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trait State[Obs, A, R, M[_]] extends AnyRef

A world should probably have a generator of states and actions... and then you can use that to get to the next thing. The state here is going to be useful in the Markov model; for the bandit we only have a single state, not that useful.

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

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

Abstract Value Members

  1. abstract def dynamics: Map[A, M[(R, This)]]

    For every action you could take, returns a generator of the next set of rewards.

    For every action you could take, returns a generator of the next set of rewards. This is a real world, or a sample model. If we want the full distribution we're going to have to build out a better interface. Good enough for now.

  2. abstract def invalidMove: M[(R, This)]
  3. abstract def observation: Obs

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  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. def act(action: A): M[(R, This)]
  5. def actions: Set[A]
  6. final def asInstanceOf[T0]: T0
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  7. def clone(): AnyRef
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  10. def finalize(): Unit
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  11. final def getClass(): Class[_]
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  13. final def isInstanceOf[T0]: Boolean
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  14. def isTerminal: Boolean

    Returns a list of possible actions to take from this state.

    Returns a list of possible actions to take from this state. To specify the terminal state, return an empty set.

  15. def mapK[N[_]](f: FunctionK[M, N])(implicit N: Functor[N]): State[Obs, A, R, N]
  16. def mapObservation[P](f: (Obs) ⇒ P)(implicit M: Functor[M]): State[P, A, R, M]
  17. def mapReward[T](f: (R) ⇒ T)(implicit M: Functor[M]): State[Obs, A, T, M]
  18. final def ne(arg0: AnyRef): Boolean
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  19. final def notify(): Unit
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  21. final def synchronized[T0](arg0: ⇒ T0): T0
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  22. def toString(): String
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  23. final def wait(): Unit
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  24. final def wait(arg0: Long, arg1: Int): Unit
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  25. final def wait(arg0: Long): Unit
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