Multiple Model Reinforcement Learning for Environments with Poissonian Time Delays
Bibliographic record
Abstract
This thesis proposes a novel algorithm for use in reinforcement learning problems where a stochastic time delay is present in an agent's reinforcement signal.In these problems, the agent does not necessarily receive a reinforcement immediately after the action that caused it.We relax previous constraints in the literature by assuming that rewards may arrive to the agent out of order or may even overlap with one another.The algorithm combines Q-learning and hypothesis testing to enable the agent to learn about the delay itself.A proof of convergence is provided.The algorithm is tested in a grid-world simulator in MATLAB, the Webots mobile-robot simulator, and in an experiment with a real e-Puck mobile robot.In each of these test beds, the algorithm is compared to Watkins' Q-learning, of which it is an extension.In all cases, the novel algorithm outperforms Q-learning in situations where reinforcements are variably delayed.noise term F (x) mapping from R n into itself (Ω, F, P ) probability space xi
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".