A Machine Learning Approach for Aerial Drones Playing the Pursuit-Evasion Game
Bibliographic record
Abstract
Drone and quadcopters have gained popularity and their full range of potential and wide-ranging application in many applications have yet to be developed.This research presents a machine-learning algorithm.With no prior knowledge of the environment, agents learn to act using reinforcement learning techniques where they observe the environment and take actions based on learning their desired strategies and interactions with the environment.This thesis investigates the use of multi-agent reinforcement learning for aerial drones.Specifically, the use of the fuzzy actor-critic learning (FACL) algorithm.In this thesis, the investigated reinforcement learning algorithm is applied to multiple coordinated aerial drones.We apply Type-1 fuzzy inference systems with reinforcement learning algorithms and show simulation results.We provide empirical results to show the simplicity and effectiveness of Type-1 fuzzy logic systems over that of Type-2 fuzzy logic systems.This method enables a pursuer quadcopter to capture an evader quadcopter in the pursuit-evasion (PE) differential game.In this application, the pursuer learns its desired strategies by interacting with the evader and learning from previous experiences.Both the critic and the actor are fuzzy inference systems (FIS).It is assumed that the pursuer
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".