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THE CONSCIOUSNESS ATLAS
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ConsciousnessLearn · Explore · Joseph Anady

TD error: δ = r + γV(s') - V(s) · @brief TD error: δ

This relation defines @brief TD error: δ in the source section “TD error: δ = r + γV(s') - V(s) · @brief TD error: δ”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation depends on the source’s stated variables and…

Implementation mathematicsSource recovered; runtime not executed
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gh-eq-11c9509649e3c747source-comment-equation
@briefTDerror:δ=r+γV(s′)−V(s)@brief TD error: δ = r + γV(s') - V(s)

Read the equation

This relation defines @brief TD error: δ in the source section “TD error: δ = r + γV(s') - V(s) · @brief TD error: δ”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation depends on the source’s stated variables and conditions; this record does not assign an unprovided physical or cognitive meaning to them.

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 @brief TD error: δ 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

  • This source expression was recovered from the pinned code revision. Its presence does not establish that the associated program was built, run, or scientifically validated.

Original context

Read the surrounding source passage
 * @brief Eligibility trace update: e(t+1) = λ·e(t) + x_pre·x_post
 */
inline double eligibility_update(double trace, double x_pre, double x_post, double lambda = 0.9) {
    return lambda * trace + x_pre * x_post;
}

/**
 * @brief TD error: δ = r + γV(s') - V(s)
 */
inline double td_error(double reward, double value_next, double value_current, double gamma_discount = 0.99) {
    return reward + gamma_discount * value_next - value_current;
}

/**
 * @brief Policy gradient: ∇J ≈ Σ(∇log π(a|s)·R)
 */
inline double policy_gradient(double action_prob, double advantage) {
    if (action_prob < 1e-10) return 0.0;
Exact original notation
@brief TD error: δ = r + γV(s') - V(s)

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