Transfer of Reinforcement Learning-Based Powertrain Controllers From Model- to Hardware-in-the-Loop
Bibliographic record
Abstract
Developing powertrain control functions is time-consuming and resource-intensive, often leading to sub-optimal solutions. Reinforcement Learning (RL) allows agents to perform complex control tasks with minimal human involvement, but is often confined to simulations due to testing costs and safety concerns. To effectively apply RL in embedded powertrain control, agents must be able to handle real-world scenarios, particularly through direct interaction with real actuators and control systems. Therefore, this research applies Transfer Learning (TL) and X-in-the-Loop (XiL) simulations to develop agents that can seamlessly transition and perform robustly in real-world environments. For transient exhaust gas re-circulation control of an internal combustion engine, the process begins with a computationally inexpensive Model-in-the-Loop (MiL) simulation to select a suitable algorithm, fine-tune hyperparameters, and conduct preliminary training. In the next step, pre-trained agents are transferred to an advanced Hardware-in-the-Loop (HiL) system with real hardware using TL for further training. Compared to agents trained entirely on HiL systems, transferred agents required significantly less real-world training time (up to$5.9$times shorter) while outperforming the series production Engine Control Unit (ECU). The results highlight that for real-world effectiveness, integrating actual hardware into training is essential, reward fine-tuning plays a critical role in optimizing these interactions, and the maturity of the policy significantly influences both training duration and overall performance.
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.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".