VizNav: A Modular Off-Policy Deep Reinforcement Learning Framework for Vision-Based Autonomous UAV Navigation in 3D Dynamic Environments
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) provide benefits through eco-friendliness, cost-effectiveness, and reduction of human risk. Deep reinforcement learning (DRL) is widely used for autonomous UAV navigation; however, current techniques often oversimplify the environment or impose movement restrictions. Additionally, most vision-based systems lack precise depth perception, while range finders provide a limited environmental overview, and LiDAR is energy-intensive. To address these challenges, this paper proposes VizNav, a modular DRL-based framework for autonomous UAV navigation in dynamic 3D environments without imposing conventional mobility constraints. VizNav incorporates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with Prioritized Experience Replay and Importance Sampling (PER) to improve performance in continuous action spaces and mitigate overestimations. Additionally, VizNav employs depth map images (DMIs) to enhance visual navigation by accurately estimating objects’ depth information, thereby improving obstacle avoidance. Empirical results show that VizNav, by leveraging TD3, improves navigation, and the inclusion of PER and DMI further boosts performance. Furthermore, the deployment of VizNav across various experimental settings confirms its flexibility and adaptability. The framework’s architecture separates the agent’s learning from the training process, facilitating integration with various DRL algorithms, simulation environments, and reward functions. This modularity creates a potential to influence RL simulation in various autonomous navigation systems, including robotics control and autonomous vehicles.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it