Publication details
SOS: Safe, Optimal and Small Strategies for Hybrid Markov Decision Processes
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | Quantitative Evaluation of Systems, 16th International Conference, QEST 2019, Glasgow, UK, September 10-12, 2019, Proceedings. |
| MU Faculty or unit | |
| Citation | |
| Doi | https://doi.org/10.1007/978-3-030-30281-8_9 |
| Description | For hybrid Markov decision processes, 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. |