AIMech AI

Replacing eligibility traces

Mathematical Methods AIMech:ReplacingEligibilityTraces DRAFT
An eligibility-trace update that replaces a visited state’s eligibility by one while geometrically attenuating other state traces, preserving recent credit eligibility without adding repeated-visit multiplicity.
Identity Notes & Concept Scope This source-scoped credit-memory component replaces visited state/selected feature traces rather than accumulating repeated-visit counts. It is distinct from a complete TD/Sarsa controller, the original Watkins hard-cut forward/delayed construction, neural MCQ-L and Peng–Williams two-error control. Existing owner264 retains general/true-online and other-variant residuals. Fresh collection on 15 October 2026 at14:35:54–14:37:34 UTC at actual merged base 73729051e58d582f36c9af9296a92b393be0a679 compared all763 native and442 pinned AIO records (4,807 identity-field occurrences,3,674 normalized values), with zero candidate collisions and15 context-only fields across eight neighboring records. Four ignored- and hidden-inclusive checkout searches retained3,967/3,981/9,015/10,567 occurrences (27,530 total), grouped into1,338 exact matching contents. Root read all39 focused candidate-bearing decoded contexts, all15 normalized contextual fields/eight definitions, all20 full matching frontier objects and the three newly merged342 definitions. This is bounded semantic inspection; root does not claim a verbatim reading of all1,084 broader decoded contexts or every repeated occurrence. All909main and393nested objects were queried recursively; historic primary/AIO and supporting inspections retain their separately documented scopes.

Claims & Grounded Evidence

5 Claims
Mechanism #state-replacing-recurrence
For a visited state s_t, replace its eligibility by e_(t+1)(s_t)=1. For every other state s, set e_(t+1)(s)=gamma*lambda*e_t(s), with gamma and lambda in [0,1]. This differs from the accumulating rule e_(t+1)(s)=gamma*lambda*e_t(s)+1{s=s_t}: another visit replaces the current state trace rather than adding to its previous value.
Mechanism #trace-as-td-credit-component
In the paper’s undiscounted absorbing-state prediction setting, the replacing trace enters the TD update as Delta V_t(s)=alpha_t(s)*[r_(t+1)+V_t(s_(t+1))-V_t(s_t)]*e_(t+1)(s), with terminal value zero. The trace determines which earlier states remain eligible for this learning signal; it is a component of the prediction update rather than a complete policy or control algorithm.
Scope #bounded-recency-with-endpoints
For traces initialized in [0,1] and q=gamma*lambda in [0,1], the replacing recurrence preserves each trace in [0,1]. After k consecutive inactive updates, a trace equals q^k times its earlier value. Thus inactive traces vanish after one update when q=0 and do not decay when q=1. These are direct consequences of reset-to-one and multiplication by q; repeated visits do not build the accumulating rule’s visit-multiplicity sum.
Scope #qualified-first-visit-correspondence
In the undiscounted absorbing prediction setting with gamma=lambda=1, repeated-batch replacing TD converges to the first-visit Monte Carlo sample estimate for sampled states with N(s)>0 under the paper’s fixed step size 0<alpha<1/N, where N is the number of training trials. Separately, offline replacing TD(1), applied after each completed trial with alpha_t(s) equal to the reciprocal number of first visits to s up through t, gives the first-visit sample-average update. These are the paper’s specified batch-limit and offline correspondences, not arbitrary finite-iteration online equalities.
Mechanism #binary-feature-control-realization
The reported CMAC control realization maintains traces for action-specific binary features. After decaying old traces, it sets active features for the chosen action to one and clears the corresponding active features for other actions; the resulting trace vector weights a later scalar TD update. This chosen control convention is distinct from the alternative discussed in the paper, which replaces a state-action pair’s trace only when that same pair recurs.

⚠️ Curated Limitations & Negative Results

Artificial Intelligence Ontology (AIO) Grounding

Mapped AIO Classes
AIO Target Class Mapping Relation Mapping Rationale
AIO:ReinforcementLearning
Reinforcement Learning
related_match A credit-assignment component used within reinforcement-learning prediction/control; the broader pinned task is neither the recurrence itself nor a guaranteed outcome.

Evidence Sources & Primary Literature

1 Source
Source ID Publication / Document Type / Access Reference & Checksum
singh-sutton1996
Satinder P. Singh, Richard S. Sutton
primary paper
full_text
ID: https://doi.org/10.1007/BF00114726
sha256:93193ac196bc…

Curation Provenance

Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-15T14:45:47Z
Updated: 2026-10-15T14:45:47Z
Source File: data/curated/ReplacingEligibilityTraces.yaml