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Full-echo Q-routing

Algorithms AIMech:FullEchoQRouting DRAFT

An information-exploration modification of tabular Q-routing that queries immediate neighbors whenever a node needs a routing decision and uses their destination-time estimates to adjust every corresponding neighbor entry, with feedback carried on a separate channel assumed not to contribute to congestion in the source model.

Identity Notes & Concept Scope This record selects the NIPS source-defined Full-echo information policy: decision-triggered immediate-neighbor querying and adjustment of all corresponding local delivery-time estimates. It is distinct from selected-neighbor baseline QRouting, measured-trip AntNet and ant-based telephone routing. The source does not fully specify alternative-link delay measurements or a complete backup/message protocol. Existing owner 267 is reused. Fresh collection on 15 October 2026 at 15:21:24–15:22:28 UTC used actual merged base 342606efadd8c2896facca6e6fa2f78e216f0a8b and compared all 766 native and 442 pinned AIO records (4,816 identity-field occurrences including local IDs; 3,680 normalized values). No canonical or explicit Greek-lambda candidate collision was found; 18 context-only fields span eight neighboring records. Four ignored- and hidden-inclusive checkout searches retained 1,353/1,360/3,385/4,292 occurrences, 10,390 total, in 557 exact contents. Root read all 41 candidate-specific decoded semantic contents, all 18 contextual identity fields/eight definitions and nine full relevant frontier objects; repeated identical HTML wrapper text was not counted as a separate scientific reading. Supporting source-agent assessment read all 445 decoded contexts and reconstructed all raw groups/ordered occurrences. All909main and393nested objects were queried mechanically. Historical source/AIO readings retain their separately documented scopes. These results are bounded to the dated corpus, four roots and semantic neighborhood, not global absence or saturation.

Claims & Grounded Evidence

4 Claims

Scope #source-defined-extension

The named Full-echo modification retains Q-routing’s local table of estimated delivery times indexed by destination and neighboring node. In the source notation, Q_x(d,y) estimates the delivery time from node x to destination d through neighbor y, including the local queue time. Full-echo changes the information-exploration and entry-adjustment policy; it is not a synonym for the original selected-neighbor-only baseline.

supports Source: Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach · Locator: Section 2, PDF p. 2 / proceedings p. 672, definition of Q_x(d,y); Section 3.2, PDF p. 6 / proceedings p. 676, greedy-update limitation and named modification.

Mechanism #decision-triggered-neighbor-query

Whenever a node using Full-echo needs to make a routing decision, it sends information requests to its immediate neighbors. Each neighbor returns a single scalar giving that neighbor’s current estimate of the total time to the destination. These responses are estimates supplied by neighboring learners, not observations of complete trips along every alternative route.

supports Source: Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach · Locator: Section 3.2, PDF p. 6 / proceedings p. 676, paragraph beginning “Instead of sending actual packets” and the description of each scalar response.

Mechanism #all-neighbor-estimate-adjustment

The returned estimates are used to adjust the local Q_x(d,y) value for each neighboring node y. This addresses the greedy baseline’s failure to refresh unused routes whose delivery times were overestimated. The source specifies this all-neighbor information policy without supplying a distinct algebraic Full-echo backup or a measurement rule for an untraversed link’s delay.

supports Source: Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach · Locator: Section 3.2, PDF p. 6 / proceedings p. 676, the unused-route overestimate example and the sentence specifying adjustment for each neighbor.

Mechanism #information-exploration-channel

Full-echo obtains information about alternatives by querying neighbors instead of deliberately forwarding actual data packets in random directions for exploration. The returned scalars use a separate channel that does not contribute to network congestion in the authors’ model. This is an explicit modeling assumption about feedback traffic, not a general zero-cost communication guarantee.

supports Source: Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach · Locator: Section 3.2, PDF p. 6 / proceedings p. 676, random-traffic congestion discussion and the separate-channel qualification in the Full-echo paragraph.

⚠️ Curated Limitations & Negative Results

The defining paragraph does not specify a separate all-neighbor update equation, how local or untraversed-link delay is obtained, exact query/response/update ordering, tie rules or a complete executable protocol. The baseline update uses experienced queue and transmission delays on the selected link; copying that observed-link target to every alternative would add an unsupported rule.
The separate feedback channel is non-congesting in the authors’ model. This does not establish zero bandwidth, latency, processing overhead or resource consumption in a deployed communication network.
The distributed environment changes with traffic and with the routing decisions made by other learners. The source does not provide a stationary single-agent Q-learning convergence theorem, universal policy-stability result, optimality certificate or loop-freedom guarantee for this modification.
The reported low-load improvement and high-load oscillation concern the paper’s simplified simulation and traffic conditions. They do not establish uniform superiority, universal instability or a numerical benchmark result reproduced here. Refreshing more estimates does not by itself guarantee better end-to-end behavior.
The July 1993 CMU-CS-93-165 report is earlier baseline context and does not specify a Full-echo procedure in its eight inspected pages. Its Q_x(y,d) argument order, displayed target without an explicit transmission term and experimental learning rate 0.7 are not silently combined with the selected NIPS account’s Q_x(d,y), explicit queue-plus-transmission term and learning rate usually 0.5 in its experiments. These are source-version boundaries, not repaired equations.
This record selects the original Full-echo modification rather than baseline Q-routing, AntNet, source-directed ant-based telephone routing or later predictive, dual, confidence-based and adaptive-parameter routing extensions. A shared reinforcement-learning relation does not make those mechanisms equivalent or resolve their separate scopes.
The declared method evidence is the official eight-page NIPS 1993 archive paper; the author CV lists the NIPS 6 proceedings publication in 1994. The separate July 1993 report and workshop references do not establish an independently verified priority date or identical method version. Full native text and selected formula/method images were inspected, without author-code execution, reconstruction of an unspecified protocol, simulator replication or certification of numerical figure values.

Artificial Intelligence Ontology (AIO) Grounding

Mapped AIO Classes

AIO Target Class	Mapping Relation	Mapping Rationale
`AIO:ReinforcementLearning` Reinforcement Learning	related_match	The source-scoped procedure adapts distributed routing decisions using local delivery-time feedback and neighboring bootstrapped estimates. The pinned AIO target is a broader learning-task classification; this algorithm-to-task relation asserts neither exact identity nor a subclass, stationary-MDP or convergence guarantee.

Evidence Sources & Primary Literature

1 Source

Source ID	Publication / Document	Type / Access	Reference & Checksum
`boyan1993-nips`	Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach Justin A. Boyan, Michael L. Littman	primary paper full_text	sha256:d85e5660aa4e…

Curation Provenance

Curator: Codex

Method: ai_assisted

Model: GPT-6

Created: 2026-10-15T15:40:28Z

Updated: 2026-10-15T15:40:28Z

Source File: data/curated/FullEchoQRouting.yaml