object Episode
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- Episode.scala
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case class
Moment[Obs, A, R, M[_]](policy: Policy[Obs, A, R, M, M], state: State[Obs, A, R, M]) extends Product with Serializable
Wrapper around a combination of state and policy.
Wrapper around a combination of state and policy. A moment in time. this wraps up a common thing that we interact with...
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def
firstVisit[Obs, A, R, M[_]](moment: Moment[Obs, A, R, M])(implicit arg0: Monad[M]): M[(Moment[Obs, A, R, M], Trajectory[Obs, A, R, M])]
Specialized version of playEpisode that only updates every first time a state is seen.
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def
playEpisode[Obs, A, R, M[_], T](moment: Moment[Obs, A, R, M], tracker: Tracker[Obs, A, R, T, M])(implicit arg0: Monad[M]): M[(Moment[Obs, A, R, M], Trajectory[Obs, A, R, M])]
Takes a policy and a starting state and returns an M containing the final policy, final state and the trajectory that got us there.
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def
playMany[Obs, A, R, M[_]](moments: List[Moment[Obs, A, R, M]])(rewardSum: (List[SARS[Obs, A, R, M]]) ⇒ R)(implicit arg0: Monad[M]): M[(List[Moment[Obs, A, R, M]], R)]
Takes a list of policy, initial state pairs and plays a single episode of a game with each of them.
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def
playManyN[Obs, A, R, M[_]](moments: List[Moment[Obs, A, R, M]], nTimes: Int)(rewardSum: (List[SARS[Obs, A, R, M]]) ⇒ R)(implicit arg0: Monad[M]): M[(List[Moment[Obs, A, R, M]], List[R])]
Takes an initial set of policies and astate...
Takes an initial set of policies and astate... we could definitely adapt this to do some serious learning on the policies, and use the MonoidAggregator stuff.
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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.