# SOS: Safe, Optimal and Small Strategies for Hybrid Markov Decision Processes

## TL;DR

This paper introduces methods to convert strategies for hybrid Markov decision processes into compact, understandable decision trees that preserve safety and near-optimality, facilitating deployment in embedded systems.

## Contribution

It presents a novel approach to learn small, safe, and near-optimal decision-tree representations of strategies from UPPAAL Stratego, enabling practical deployment.

## Key findings

- Decision trees are significantly smaller and more understandable.
- Guarantees on safety and near-optimality are maintained.
- Trade-offs between size and optimality can be controlled.

## Abstract

For hybrid Markov decision processes, UPPAAL Stratego can compute strategies that are safe for a given safety property and (in the limit) optimal for a given cost function. Unfortunately, these strategies cannot be exported easily since they are computed as a very long list. In this paper, we demonstrate methods to learn compact representations of the strategies in the form of decision trees. These decision trees are much smaller, more understandable, and can easily be exported as code that can be loaded into embedded systems. Despite the size compression and actual differences to the original strategy, we provide guarantees on both safety and optimality of the decision-tree strategy. On the top, we show how to obtain yet smaller representations, which are still guaranteed safe, but achieve a desired trade-off between size and optimality.