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.
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- State.scala
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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.
- abstract def invalidMove: M[(R, This)]
- abstract def observation: Obs
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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.
- def mapK[N[_]](f: FunctionK[M, N])(implicit N: Functor[N]): State[Obs, A, R, N]
- def mapObservation[P](f: (Obs) ⇒ P)(implicit M: Functor[M]): State[P, A, R, M]
- def mapReward[T](f: (R) ⇒ T)(implicit M: Functor[M]): State[Obs, A, T, M]
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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.