Abstract
We review recent progress and open issues in the approximate solution of deterministic and stochastic optimization problems using rollout algorithms. These algorithms start with a heuristic policy and try to improve on that policy using on-line learning and simulation. They are related to dynamic programming and they are based on policy iteration ideas. Their attractive aspects are simplicity, broad applicability, and suitability for on-line implementation. While they do not aspire to optimal performance, rollout algorithms typically result in a consistent and substantial improvement over the underlying heuristic.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 448-449 |
| Number of pages | 2 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 1 |
| State | Published - 1999 |
| Externally published | Yes |
| Event | The 38th IEEE Conference on Decision and Control (CDC) - Phoenix, AZ, USA Duration: Dec 7 1999 → Dec 10 1999 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization