Enum cisat::OperationalLearning[][src]

#[non_exhaustive]pub enum OperationalLearning {
    Multinomial {
        learning_rate: f64,
        initial_learning_matrix: Vec<f64>,
    },
    Markov {
        learning_rate: f64,
        initial_learning_matrix: Vec<Vec<f64>>,
    },
    HiddenMarkov {
        learning_rate: f64,
        initial_transition_matrix: Vec<Vec<f64>>,
        initial_emission_matrix: Vec<Vec<f64>>,
    },
    None,
}

This enum contains options for how the agent learns

Variants (Non-exhaustive)

Non-exhaustive enums could have additional variants added in future. Therefore, when matching against variants of non-exhaustive enums, an extra wildcard arm must be added to account for any future variants.
Multinomial

Reinforcement over the action set as a multinomial distribution

Fields of Multinomial

learning_rate: f64

Learning rate

initial_learning_matrix: Vec<f64>

Initial move weights

Markov

Reinforcement over a sequence-based action set

Fields of Markov

learning_rate: f64

Learning rate

initial_learning_matrix: Vec<Vec<f64>>

Initial transition matrix

HiddenMarkov

Reinforcement over an HMM-based action set

Fields of HiddenMarkov

learning_rate: f64

Learning rate

initial_transition_matrix: Vec<Vec<f64>>

Initial transition matrix

initial_emission_matrix: Vec<Vec<f64>>

Initial emission matrix

None

Do you want dumb agents? This is how you get dumb agents.

Trait Implementations

impl Clone for OperationalLearning[src]

impl Debug for OperationalLearning[src]

Auto Trait Implementations

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impl<T> Any for T where
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impl<T> From<T> for T[src]

impl<T, U> Into<U> for T where
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impl<T> Pointable for T

type Init = T

The type for initializers.

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type Error = <U as TryFrom<T>>::Error

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