Adaptive reinforcement Q-Learning algorithm for swarm-robot system using pheromone mechanism
Bibliographic record
Abstract
The states and actions of the robots in uncertain environments are continuous, which will easily lead to the problem of slow learning speed and the combinatorial explosion issue of the reinforcement learning. Ant colony optimization (ACO) is an evolution algorithm based on swarm mechanism that takes full advantage of the pheromone mechanism to simplify the information sharing and collaborative issues between the swarm individuals. Adaptive robust reinforcement Q-Learning algorithm based on ACO is proposed from two parts: adaptive discretization part and pheromone part. Firstly, adaptive discretization of the continuous input space is realized by the self-organizing neural network. Secondly, the pheromone mechanism of ACO is introduced to improve the traditional reinforcement learning process, which can improve the adaptive capabilities of the system and reduce the space complexity of accelerating the learning speed of the swarm robots. Player/Stage is used as the simulation platform to verify the proposed algorithm. The results show proposed algorithm has efficiency and adaptive capacity in the swarm robotic system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".