metadata
license: mit
task_categories:
- reinforcement-learning
- robotics
language:
- en
tags:
- drone-navigation
- rl-dataset
- threejs
- ppo
- telemetry
- pathfinding
DRONE FSD DATASET
Single training run (1 epoch, 4 iterations, 198 steps) of a drone navigating a 60×60 room with 15 static + 12 floating obstacles.
This dataset was generated with the MIRROR IDE by webXOS. Download the app in the /mirror/ folder to train your own similar datasets.
Final performance (after 2456 frames):
- Best time: 43.821 s
- Success rate: 0.0% (reached SE corner in best run but did not complete full pattern)
- Collisions: 0 in final recorded path
- Avg reward: 0.0732
- Cumulative reward: 49.24
- Final exploration rate: 0.784
- Final learning rate: 5.40e-4
Network
- Architecture:
[256 → 128 → 64 → 32](MLP policy/value heads) - Exported: 2026-01-17 03:32 UTC
Files
| File | Description | Size |
|---|---|---|
enhanced_network.json |
Final policy weights + shapes + LR | ~small |
metadata.json |
Training summary & config | ~small |
successful_paths.json |
Best 3 partial successes (times, paths) | ~small |
enhanced_telemetry.jsonl |
Full per-frame telemetry (2456 lines) | ~2.4 MB |
enhanced_telemetry.csv |
Same data in CSV format | ~1.8 MB |
training_experiences.jsonl |
PPO-style transitions (state, action, reward, next) | ~1.2 MB |
Environment
- Room: 60 units
- Difficulty: 1
- Obstacles: 15 static + 12 floating (0.2–0.5 speed, bounce energy 0.8)
- Pattern targets: NW → SE → NE → SW → CENTER
- Reward: mostly distance-based + small shaping
Intended Use
- Analyze early-stage PPO behavior on 3D continuous control
- Study exploration vs exploitation trade-off (ε still ~78% at end)
- Visualize drone trajectories in Three.js / Unity / similar
- Baseline for future drone racing / obstacle avoidance models