Reinforcement Learning with Overcomplete Sparse Codes for Natural Image Control
A new arXiv preprint (2412.08893) introduces a reinforcement learning framework for optimal control using natural images. The authors formalize the problem and derive conditions for image sufficiency in policy implementation. They demonstrate that encoding images as overcomplete sparse codes enables efficient solving of control tasks orders of magnitude larger than those using complete codes. A novel benchmark scaling to many states and long horizons is presented.
Key facts
- arXiv preprint 2412.08893
- Optimal control with natural images
- Reinforcement learning framework
- Overcomplete sparse codes for image encoding
- New benchmark for large-scale tasks
- Orders of magnitude larger than complete code solutions
- Conditions for image sufficiency derived
- Efficient policy implementation demonstrated
Entities
Institutions
- arXiv