Create README.md

README.md

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+---
+license: mit
+task_categories:
+  - reinforcement-learning
+  - robotics
+language:
+  - en
+tags:
+  - drone-navigation
+  - rl-dataset
+  - threejs
+  - ppo
+  - telemetry
+  - pathfinding
+---
+
+# Drone RL Training Run – Pattern: NW→SE→NE→SW→CENTER
+
+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