AIMech AI

WIS-TO-GTD(lambda)

Algorithms AIMech:WISTrueOnlineGTDLambda DRAFT
A linear off-policy state-value prediction adaptation of true online GTD(lambda) that derives componentwise main-weight step sizes from feature usage, retains a scalar auxiliary step size, and couples main and auxiliary Dutch traces with a gradient-correction trace.
Identity Notes & Concept Scope The actual 2026-10-15 post-cohort 348 collection at commit 3fae62c1e83faa63da81229ab0d1268db1bfe8dd covers 779 maintained and 442 generated records. Four fixed-string, word-boundary searches included ignored and hidden files across the original checkout and three retained discovery/recovery checkouts. All 7647 occurrences were reconstructed into 2035 distinct contents, freshly read by two attributed supporting readers; root read the 79 candidate-focused contents, all 41 normalized context fields/16 adjacent definitions and the matching frontier context. Normalized IDs, local IDs, names, aliases and scoped synonyms (4855 fields) yielded no candidate collision for the selected name, ID or explicit source/code spellings. WIS-TO-GTD references in earlier reports, reviews and generated pages are dated residuals, exclusions or this separately assessed draft, not a competing maintained record. WIS-GTD, WIS-TD, usage-based ordinary/true-online TD, base true-online GTD, Dutch/replacing components and Sarsa/Peng/Watkins control records retain distinct declared scopes. Reuse existing partial owner 264; preserve its ten prior IDs, six resolutions and all 908 other main plus 393 nested entries. No frontier leaf is appended. This is bounded identity evidence, not global absence, independent approval or family exhaustion.

Claims & Grounded Evidence

6 Claims
Mechanism #linear-off-policy-host-adaptation
WIS-TO-GTD(lambda) estimates a target policy state value V_t(s)=theta_t^T*phi(s) from behavior-policy transitions. The source uses fixed linear features, current action ratios rho_t=pi(A_t|S_t)/b(A_t|S_t), state-dependent discount gamma and bootstrapping lambda in [0,1]. Its usage adaptation replaces the main scalar step size of the true-online GTD host by a component vector while retaining scalar beta for the auxiliary weights. This is prediction machinery, not an action-selection or policy-improvement rule.
Mechanism #usage-state-and-explicit-reciprocal-versions
Let ⊙ denote componentwise multiplication, h_t=1-eta*(phi_t⊙phi_t) and q_t=gamma_t*lambda_t. Update u_(t+1)=h_t⊙u_t+rho_t*(phi_t⊙phi_t)+(rho_t-1)*q_t*(h_t⊙v_t) and v_(t+1)=rho_t*q_t*(h_t⊙v_t)+rho_t*(phi_t⊙phi_t), using old v_t in both right sides. The main step vector alpha_(t+1) is computed from new u_(t+1). The paper assigns a reciprocal at zero the value zero. The pinned WTOGTD code instead replaces an exactly zero denominator by one before taking its reciprocal, giving step size one at that component. These are explicitly different boundary conventions.
Mechanism #three-traces-with-pinned-correction-history
In the pinned code, with a_t=alpha_(t+1)⊙phi_t and q_t=gamma_t*lambda_t, the main trace is e_t=rho_t*[q_t*e_(t-1)+(1-rho_t*q_t*phi_t^T*e_(t-1))*a_t]. The auxiliary trace is e_w,t=rho_(t-1)*q_t*e_w,(t-1)+beta*(1-rho_(t-1)*q_t*phi_t^T*e_w,(t-1))*phi_t. The accumulating correction trace is e_grad,t=rho_t*(q_t*e_grad,(t-1)+phi_t). Thus the first and third traces use current rho, while the auxiliary Dutch trace uses stored previous rho. The code and the 2014 host use the correction trace’s own old history; the printed 2015 Algorithm 3 instead displays old main e_(t-1) in that recurrence. The selected explicit correction-history equation here is the pinned-code interpretation.
Mechanism #pre-update-predictions-and-coupled-weights
Form delta_t=R_(t+1)+gamma_(t+1)*theta_t^T*phi_(t+1)-theta_t^T*phi_t before updating weights. With o_t the stored previous next-state prediction, the pinned code updates theta_(t+1)=theta_t+delta_t*e_t+(theta_t^T*phi_t-o_t)*(e_t-rho_t*a_t)-gamma_(t+1)*(1-lambda_(t+1))*(w_t^T*e_grad,t)*(alpha_(t+1)⊙phi_(t+1)), and w_(t+1)=w_t+rho_t*delta_t*e_w,t-beta*(w_t^T*phi_t)*phi_t. Both right sides use old w_t and the same pre-update delta. It then stores rho_t and the pre-update theta_t^T*phi_(t+1). Along a contiguous feature trajectory, o_t equals theta_(t-1)^T*phi_t, rather than a next prediction recomputed after the current weight update.
Scope #qualified-zero-lambda-and-on-policy-reductions
When all lambda values are zero, the traces reduce to e_t=rho_t*(alpha_(t+1)⊙phi_t), e_w,t=beta*phi_t and e_grad,t=rho_t*phi_t, and the prediction-difference term vanishes. With matched usage, reciprocal and initialization conventions, the resulting update is WIS-GTD(0). When all rho values are one, beta=0 and w_0=0, the auxiliary weights remain zero and the main update reduces to usage-based true-online TD under matched feature, reciprocal and boundary conventions. Beta=0 alone with arbitrary nonzero w_0 does not generally remove the main auxiliary correction.
Scope #feature-linear-update-bookkeeping
At a fixed feature dimension n, the pinned update maintains a fixed number of length-n usage, trace and weight vectors and uses elementwise arithmetic and inner products. Its dense update arithmetic and storage are O(n) per transition, excluding feature construction, environment interaction and evaluation. This bookkeeping statement is not a wall-clock benchmark or an active-feature-only complexity guarantee.

⚠️ 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 The algorithm predicts target-policy state values from reward transitions sampled under a behavior policy, supplying prediction machinery used in reinforcement learning. The pinned AIO target describes a broader exploration/performance-oriented task. This is a qualified association, not exact identity, a subclass assertion, action selection, policy improvement or convergence.

Evidence Sources & Primary Literature

3 Sources
Source ID Publication / Document Type / Access Reference & Checksum
mahmood2015-author
A. Rupam Mahmood, Richard S. Sutton
primary paper
full_text
ID: https://proceedings.mlr.press/r13/mahmood15a.html
sha256:9f30489028c8…
vanhasselt2014-author
Hado van Hasselt, A. Rupam Mahmood, Richard S. Sutton
primary paper
full_text
sha256:b2d5ecc4acea…
mahmood2015-wtogtd-code
A. Rupam Mahmood
official documentation
documentation
ID: https://github.com/armahmood/wis-td-experiments/blob/d73d441ca34c546cae79c9289104a68238c9b7ce/pysrc/algorithms/tdprediction/offpolicy/wtogtd.py
sha256:e394f9f4353a…

Curation Provenance

Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-15T19:46:10Z
Updated: 2026-10-15T19:46:10Z
Source File: data/curated/WISTrueOnlineGTDLambda.yaml