Embedding Reinforcement Learning in Simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reinforcement learning (RL) usage in the industrial domain is on the rise since the shift towards Industry4.0 systems. The need for faster adaptive systems is encouraging manufacturers to invest more in artificial intelligence (AI) technologies. Our aim is to add better intelligence into simulation tools by embedding RL capabilities. Discrete event simulations have been used for decision support in manufacturing systems for decades. New simulation tools such as AnyLogic have improved significantly in the past few years. In this paper, we built a RL library for AnyLogic simulation models. The RL library is developed for model designers, who may not be experts in the field of RL. We applied our RL library on a real-world use case model for truck dispatching problem. The results show the benefits of using RL in real-world problems to find better dispatching policies. Additionally, the visualization capabilities of AnyLogic enabled us to explain the RL agent's reaction to changes in the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it