# Data-driven Rollout for Deterministic Optimal Control

@article{Li2021DatadrivenRF, title={Data-driven Rollout for Deterministic Optimal Control}, author={Yuchao Li and Karl Henrik Johansson and Jonas M{\aa}rtensson}, journal={2021 60th IEEE Conference on Decision and Control (CDC)}, year={2021}, pages={2169-2176} }

We consider deterministic infinite horizon optimal control problems with nonnegative stage costs. We draw inspiration from learning model predictive control scheme designed for continuous dynamics and iterative tasks, and propose a rollout algorithm that relies on sampled data generated by some base policy. The proposed algorithm is based on value and policy iteration ideas, and applies to deterministic problems with arbitrary state and control spaces, and arbitrary dynamics. It admits…

## 3 Citations

### Policy iteration for the deterministic control problems - a viscosity approach

- Mathematics, Computer ScienceArXiv
- 2023

It is proved that PI for the semi-discrete scheme converges exponentially fast, and a bound is provided on the error induced by the Semi-Discrete Scheme.

### Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control

- Computer ScienceArXiv
- 2021

This paper shows that the principal AlphaZero/TD-Gammon ideas of approximation in value space and rollout apply very broadly to deterministic and stochastic optimal control problems, involving both discrete and continuous search spaces.

### Newton’s method for reinforcement learning and model predictive control

- Computer ScienceResults in Control and Optimization
- 2022

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