SOS: Safe, Optimal and Small Strategies for Hybrid Markov Decision Processes
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Jun 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 4.
9 papers.
- 2021dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Expertscited by 14
- 2023Shielded Reinforcement Learning for Hybrid Systemscited by 8
- 2022Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forcescited by 5
- 2026Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MCcited by 3
- 2025Explaining Control Policies through Predicate Decision Diagramscited by 1
- 2024A hierarchical control framework for autonomous decision-making systems: Integrating HMDP and MPCcited by 0
- 2026COOL-MC: Verifying and Explaining RL Policies for Platelet Inventory Managementcited by 0
- 2026Explainably Safe Reinforcement Learningcited by 0
- 2026dtControl2+varepsilon: Trading Optimality for Explainability in MDPs via Decision Treescited by 0
- 2026dtControl2+varepsilon: Trading Optimality for Explainability in MDPs via Decision Trees28 Jul 2026
- 2026Explainably Safe Reinforcement Learning3 Jun 2026
- 2026COOL-MC: Verifying and Explaining RL Policies for Platelet Inventory Management2 Mar 2026
- 2026Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC16 Feb 2026
- 2025Explaining Control Policies through Predicate Decision Diagrams9 Mar 2025
- 2024A hierarchical control framework for autonomous decision-making systems: Integrating HMDP and MPC12 Jan 2024
- 2023Shielded Reinforcement Learning for Hybrid Systems28 Aug 2023
- 2022Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forces26 Aug 2022
- 2021dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts15 Jan 2021