Enum OperationalLearning

#[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

learning_rate: f64

Learning rate

initial_learning_matrix: Vec<f64>

Initial move weights

Markov

Reinforcement over a sequence-based action set

Fields

learning_rate: f64

Learning rate

initial_learning_matrix: Vec<Vec<f64>>

Initial transition matrix

HiddenMarkov

Reinforcement over an HMM-based action set

Fields

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

fn clone(&self) -> OperationalLearning

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for OperationalLearning

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.