# Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation

## TL;DR

This paper introduces a method to compute safe initial policies for reinforcement learning in uncertain systems using kernelized Lipschitz estimation and semidefinite programming, ensuring safety and stability.

## Contribution

It presents a novel approach combining kernelized Lipschitz estimation with semidefinite programming to generate safe, stabilizing initial policies for approximate dynamic programming.

## Key findings

- Provides high-probability guarantees for admissible policies.
- Ensures exponential stability of the closed-loop system.
- Enables safe initialization in reinforcement learning.

## Abstract

We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete-time dynamics. We employ kernelized Lipschitz estimation and semidefinite programming for computing admissible initial control policies with provably high probability. Such admissible controllers enable safe initialization and constraint enforcement while providing exponential stability of the equilibrium of the closed-loop system.