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 Keywordsquadcopters, FACL, Q-learning, reinforcement learning.6.28 The paths of the pursuer and evader at the 1200th epoch.The evader (0,0,0) is black, pursuer (6,6,6) is blue.Evader was captured by pursuer. . . . . . .103 6.29 Simulation results of Membership Functions at 2 MFs, 3 MFs, 5 MFs, 7 MFs and 10 MFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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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) | 0.000 | 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) | 0.002 | 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".