Joint Trajectory and Radio Resource Optimization for Autonomous Mobile Robots Exploiting Multi-Agent Reinforcement Learning
Bibliographic record
Abstract
Rapid and efficient sensor data acquisition plays a critical role in the decision-making process of each robot in a multi-robot smart factory. This paper investigates the trajectory design of autonomous mobile robots (AMRs) and communication resource allocation problems in industrial Internet of Things. Specifically, by exploiting both power and spatial domains, we adopt non-orthogonal multiple access to improve network connectivity in a spectrum-efficient manner, while the multi-antenna technique is employed to enhance diversity gain. The average sum rate is maximized by jointly optimizing the transmit power of sensors and the trajectory of AMRs. To deal with prior knowledge and dynamic channel conditions, we reformulate the long-term maximization problem as a Markov decision process, and further develop a provably efficient multi-agent reinforcement learning algorithm with a near-optimal regret bound. Our theoretical analysis reveals that both the decentralized execution and the experience exchange method are beneficial to accelerate convergence. Simulation results show that our proposed algorithm can reduce at least 80% convergence time compared to the centralized baseline, and can gain better rewards than the conventional$\epsilon $-greedy exploration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".