Sequential $Q$-Learning With Kalman Filtering for Multirobot Cooperative Transportation
Bibliographic record
Abstract
This paper presents a modified, distributed Q-learning algorithm, termed as sequential Q-learning with Kalman filtering (SQKF), for decision making associated with multirobot cooperation. The SQKF algorithm developed here has the following characteristics. 1) The learning process is arranged in a sequential manner (i.e., the robots will not make decisions simultaneously, but in a predefined sequence) so as to promote cooperation among robots and reduce theirQ-learning spaces. 2) A robot will not update its Q-values with observed global rewards. Instead, it will employ a specific Kalman filter to extract its real local reward from the global reward, thereby updating its Q-table with this local reward. The new SQKF algorithm is intended to solve two problems in multirobot Q-learning: credit assignment and behavior conflicts. The detailed procedure of the SQKF algorithm is presented, and its application is illustrated using a prototype multirobot experimental system. The experimental results show that the algorithm has better performance than the conventional single-agent Q-learning algorithm or the team Q-learning algorithm in the multirobot domain.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".