Multi‐agent reinforcement learning for process control: Exploring the intersection between fields of reinforcement learning, control theory, and game theory
Bibliographic record
Abstract
Abstract The application of reinforcement learning (RL) in process control has garnered increasing research attention. However, much of the current literature is focused on training and deploying a single RL agent. The application of multi‐agent reinforcement learning (MARL) has not been fully explored in process control. This work aims to: (i) develop a unique RL agent configuration that is suitable in a MARL control system for multiloop control, (ii) demonstrate the efficacy of MARL systems in controlling multiloop process that even exhibit strong interactions, and (iii) conduct a comparative study of the performance of MARL systems trained with different game‐theoretic strategies. First, we propose a design of an RL agent configuration that combines the functionalities of a feedback controller and a decoupler in a control loop. Thereafter, we deploy two such agents to form a MARL system that learns how to control a two‐input, two‐output system that exhibits strong interactions. After training, the MARL system shows effective control performance on the process. With further simulations, we examine how the MARL control system performs with increasing levels of process interaction and when trained with reward function configurations based on different game‐theoretic strategies (i.e., pure cooperation and mixed strategies). The results show that the performance of the MARL system is weakly dependent on the reward function configuration for systems with weak to moderate loop interactions. The MARL system with mixed strategies appears to perform marginally better than MARL under pure cooperation in systems with very strong loop interactions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".