Spaces:
Sleeping
Sleeping
| title: Car Racing Agent | |
| emoji: 🏎️ | |
| colorFrom: yellow | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 6.12.0 | |
| app_file: app.py | |
| pinned: true | |
| license: apache-2.0 | |
| tags: | |
| - reinforcement-learning | |
| - curriculum-learning | |
| - torchrl | |
| - ppo | |
| - openenv | |
| - pygame | |
| short_description: A PPO agent trained from scratch on 10 curriculum tracks. | |
| # Car Racing Agent | |
| Live demo of a PPO car-racing agent trained from scratch across a 10-track | |
| curriculum — zero crashes on the full curriculum. | |
| - **Blog post:** <https://huggingface.co/blog/NirmalPratheep/curriculum-car-racer> | |
| - **Source / training code:** <https://github.com/NirmalPratheep/curriculum-car-racer> | |
| - **OpenEnv Student Challenge 2026** submission. | |
| ## How to use | |
| 1. Pick a track from the dropdown (Track 01 → Track 10). | |
| 2. Click **Reset** to spawn the agent. | |
| 3. Click **Step ×20** to advance physics a chunk at a time, or | |
| **Auto-Drive** to run a full lap and get a replay video. | |
| The orange-bordered thumbnail is the actual 64×64 egocentric image the | |
| CNN receives every step — always rotated so the car faces up. | |