object Evaluator
Contains traits and instances for the two evaluation methods.
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- Evaluator.scala
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class
FromAction[Obs, A, R, G, S[_], F[_, _, _, _, *[_]]] extends AnyRef
Builder class that manages conversion of an evaluate.ActionValue instance into either an evaluate.ActionValue or evaluate.StateValue instance.
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class
FromState[Obs, A, R, G, S[_], F[_, _, _, _, *[_]]] extends AnyRef
Builder class that manages conversion of an evaluate.StateValue instance into either an evaluate.ActionValue or evaluate.StateValue instance.
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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==(arg0: Any): Boolean
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- def action[Obs, A, R, G, S[_]]: FromAction[Obs, A, R, G, S, ActionValue]
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def
asInstanceOf[T0]: T0
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def
bellman[Obs, A, R, G, M[_], S[_]](valueFn: StateValueFn[Obs, G], policy: Policy[Obs, A, R, M, S], prepare: (R) ⇒ G, merge: (G, G) ⇒ G)(implicit arg0: DModule[G], arg1: Expectation[M], arg2: Expectation[S]): StateValue[Obs, A, R, G, S]
The full bellman estimation, where we know the dynamics of the policy and of the system.
The full bellman estimation, where we know the dynamics of the policy and of the system.
Could also be defined by
state.byPolicy(policy).byStateValue(prepare, merge).apply(valueFn)
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clone(): AnyRef
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eq(arg0: AnyRef): Boolean
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def
oneAhead[Obs, A, R, G, M[_], S[_]](valueFn: StateValueFn[Obs, G], prepare: (R) ⇒ G, merge: (G, G) ⇒ G)(implicit arg0: DModule[G], arg1: Expectation[S]): ActionValue[Obs, A, R, G, S]
Evaluator that uses a world's dynamics to estimate the value of a given action.
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def
state[Obs, A, R, G, S[_]]: FromState[Obs, A, R, G, S, StateValue]
This is my attempt at getting a better builder syntax going!
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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.