case class MapState[Obs, A, R, S[_]](observation: Obs, rewards: Map[A, S[R]], penalty: S[R], step: (Obs, A, R, S[R]) ⇒ (Obs, S[R]))(implicit evidence$2: Functor[S]) extends State[Obs, A, R, S] with Product with Serializable
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Instance Constructors
- new MapState(observation: Obs, rewards: Map[A, S[R]], penalty: S[R], step: (Obs, A, R, S[R]) ⇒ (Obs, S[R]))(implicit arg0: Functor[S])
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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def
act(action: A): S[(R, This)]
- Definition Classes
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def
actions: Set[A]
- Definition Classes
- State
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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def
dynamics: Map[A, S[(R, This)]]
For every action you could take, returns a generator of the next set of rewards.
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final
def
eq(arg0: AnyRef): Boolean
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def
finalize(): Unit
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def
getClass(): Class[_]
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- val invalidMove: S[(R, This)]
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final
def
isInstanceOf[T0]: Boolean
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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.
- Definition Classes
- State
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def
mapK[N[_]](f: FunctionK[S, N])(implicit N: Functor[N]): State[Obs, A, R, N]
- Definition Classes
- State
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def
mapObservation[P](f: (Obs) ⇒ P)(implicit M: Functor[S]): State[P, A, R, S]
- Definition Classes
- State
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def
mapReward[T](f: (R) ⇒ T)(implicit M: Functor[S]): State[Obs, A, T, S]
- Definition Classes
- State
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
- Definition Classes
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- @native()
- val observation: Obs
- val penalty: S[R]
- val rewards: Map[A, S[R]]
- val step: (Obs, A, R, S[R]) ⇒ (Obs, S[R])
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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final
def
wait(): Unit
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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.