distribution_l0_morphology (faber_neuroevolution v1.2.4)

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Distribution Silo L0 Morphology - TWEANN sensor/actuator definitions.

Part of the Liquid Conglomerate v2 architecture. Defines the neural network morphology for the Distribution Silo's L0 load balancing controller.

Architecture

L0 is a TWEANN (Topology and Weight Evolving Artificial Neural Network) that: - Takes 14 sensor inputs (normalized 0.0-1.0) - Produces 10 actuator outputs (distribution decisions) - Has 8 hyperparameters that L1 can tune - Has 5 L1 hyperparameters that L2 can tune

Time Constant

tau_L0 = 1 second (real-time routing decisions)

Note: The Distribution Silo operates on wall-clock time for networking decisions, not evaluation counts like the Task Silo.

Summary

Functions

Number of actuators (neural network outputs).

Ordered list of actuator names.

Get specification for an actuator.

Get bounds for L0 hyperparameters.

Get default values for L0 hyperparameters.

Get bounds for L1 hyperparameters.

Get default values for L1 hyperparameters.

Get specification for an L0 hyperparameter.

List of L0 hyperparameter names.

Get specification for an L1 hyperparameter.

List of L1 hyperparameter names.

Number of sensors (neural network inputs).

Ordered list of sensor names.

Get specification for a sensor.

L0 time constant - 1 second for real-time routing decisions.

L1 time constant - 10 seconds for load balancing adaptation.

L2 time constant - 1 minute for topology learning.

Functions

actuator_count()

-spec actuator_count() -> pos_integer().

Number of actuators (neural network outputs).

actuator_names()

-spec actuator_names() -> [atom()].

Ordered list of actuator names.

actuator_spec(_)

-spec actuator_spec(atom()) -> map() | undefined.

Get specification for an actuator.

get_l0_bounds()

-spec get_l0_bounds() -> map().

Get bounds for L0 hyperparameters.

get_l0_defaults()

-spec get_l0_defaults() -> map().

Get default values for L0 hyperparameters.

get_l1_bounds()

-spec get_l1_bounds() -> map().

Get bounds for L1 hyperparameters.

get_l1_defaults()

-spec get_l1_defaults() -> map().

Get default values for L1 hyperparameters.

l0_hyperparameter_spec(_)

-spec l0_hyperparameter_spec(atom()) -> map() | undefined.

Get specification for an L0 hyperparameter.

l0_hyperparameters()

-spec l0_hyperparameters() -> [atom()].

List of L0 hyperparameter names.

l1_hyperparameter_spec(_)

-spec l1_hyperparameter_spec(atom()) -> map() | undefined.

Get specification for an L1 hyperparameter.

l1_hyperparameters()

-spec l1_hyperparameters() -> [atom()].

List of L1 hyperparameter names.

Note: These are called "meta-parameters" from L1's perspective, but "hyperparameters" from L2's perspective.

sensor_count()

-spec sensor_count() -> pos_integer().

Number of sensors (neural network inputs).

sensor_names()

-spec sensor_names() -> [atom()].

Ordered list of sensor names.

sensor_spec(_)

-spec sensor_spec(atom()) -> map() | undefined.

Get specification for a sensor.

tau_l0()

-spec tau_l0() -> pos_integer().

L0 time constant - 1 second for real-time routing decisions.

tau_l1()

-spec tau_l1() -> pos_integer().

L1 time constant - 10 seconds for load balancing adaptation.

tau_l2()

-spec tau_l2() -> pos_integer().

L2 time constant - 1 minute for topology learning.