Environment-aspect loss aggregation

Read the equation

Adds the layer’s per-aspect losses with λ weights. Every aspect can contribute to the same layer objective, avoiding an implicit assumption that each layer observes only one isolated part of the environment.

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

1. Read N1 (Physical): L_1* as the quantity being defined or updated by this relation.

2. Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.

3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.

Original context

▼               ▼  ▼   ▼  ▼      ▼    ▼  ▼               ▼
7 LAYER–NEURONS N_ℓ (MACRO NEURONS)

N1 (Physical):   L_1* = Σ_a λ_{1a} L_{1,a}*(S_1',D_1'; E^(a))
N2 (Link):       L_2* = Σ_a λ_{2a} L_{2,a}*(S_2',D_2'; E^(a))
N3 (Network):    L_3* = Σ_a λ_{3a} L_{3,a}*(S_3',D_3'; E^(a))
N4 (Transport):  L_4* = Σ_a λ_{4a} L_{4,a}*(S_4',D_4'; E^(a))
N5 (Session):    L_5* = Σ_a λ_{5a} L_{5,a}*(S_5',D_5'; E^(a))
N6 (Presentation):L_6* = Σ_a λ_{6a} L_{6,a}*(S_6',D_6'; E^(a))
N7 (Application): L_7* = Σ_a λ_{7a} L_{7,a}*(S_7',D_7'; E^(a))

N1 (Physical):   L_1* = Σ_a λ_{1a} L_{1,a}*(S_1',D_1'; E^(a))

Related builds