Path Following Control for UAV Using Deep Reinforcement Learning Approach
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have been extensively used in civil and industrial applications due to the rapid development of the guidance, navigation and control (GNC) technologies. Especially, using deep reinforcement learning methods for motion control acquires a major progress recently, since deep [Formula: see text]-learning algorithm has been successfully applied to the continuous action domain problem. This paper proposes an improved deep deterministic policy gradient (DDPG) algorithm for path following control problem of UAV. A specific reward function is designed for minimizing the cross-track error of the path following problem. In the training phase, a double experience replay buffer (DERB) is used to increase the learning efficiency and accelerate the convergence speed. First, the model of UAV path following problem has been established. After that, the framework of DDPG algorithm is constructed. Then the state space, action space and reward function of the UAV path following algorithm are designed. DERB is proposed to accelerate the training phase. Finally, simulation results are carried out to show the effectiveness of the proposed DERB–DDPG method.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".