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trait Policy[A, R, This <: Policy[A, R, This]] extends Learner[A, R, This] with Decider[A, R, Generator]

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 R - reward This - policy

Source
Policy.scala
Linear Supertypes
Decider[A, R, Generator], Learner[A, R, This], AnyRef, Any
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Abstract Value Members

  1. abstract def choose(state: State[A, R]): Generator[A]
    Definition Classes
    Decider
  2. abstract def learn(state: State[A, R], action: A, reward: R): This

    OR does this information go later? A particular policy should get to witness the results of a decision...

    OR does this information go later? A particular policy should get to witness the results of a decision... but instead of a reward it might be a particular long term return.

    And each of the policies needs to have some array or something that it is using to track all of these state values.

    SO THIS might not be great. But at this state, if you take this action, you get this reward. That's the note.

    This of course might need to be a monadic response.

    Definition Classes
    Learner

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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  5. def clone(): AnyRef
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  8. def finalize(): Unit
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  12. final def ne(arg0: AnyRef): Boolean
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  17. final def wait(): Unit
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  18. final def wait(arg0: Long, arg1: Int): Unit
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Inherited from Decider[A, R, Generator]

Inherited from Learner[A, R, This]

Inherited from AnyRef

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