Renewal Monte Carlo: Renewal Theory Based Reinforcement Learning
Bibliographic record
Abstract
An online reinforcement learning algorithm called renewal Monte Carlo (RMC) is presented. RMC works for infinite horizon Markov decision processes with a designated start state. RMC is a Monte Carlo algorithm that retains the key advantages of Monte Carlo-viz., simplicity, ease of implementation, and low bias-while circumventing the main drawbacks of Monte Carlo- viz., high variance and delayed updates. Given a parameterized policyπθ, the algorithm consists of three parts: estimating the expected discounted reward Rθand the expected discounted time Tθover a regenerative cycle; estimating the derivatives ΔθRθand ΔθTθ; and updating the policy parameters using stochastic approximation to find the roots of RθΔθTθ- TθΔθRθ. It is shown that under mild technical conditions, RMC converges to a locally optimal policy. It is also shown that RMC works for postdecision state models as well. An approximate version of RMC is proposed where a regenerative cycle is defined as successive visits to a prespecified “renewal set”. It is shown that if the value function of the system is locally Lipschitz on the renewal set, then RMC converges to an approximate locally optimal policy. Three numerical experiments are presented to illustrate RMC and compare it with other state-of-the-art reinforcement learning algorithms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".