Sequential $Q$-Learning With Kalman Filtering for Multirobot Cooperative Transportation
Why this work is in the frame
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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 their <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -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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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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