Verse: A Python library for reasoning about multi-agent hybrid system scenarios
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Abstract (arXiv)
We present the Verse library with the aim of making hybrid system verification more usable for multi-agent scenarios. In Verse, decision making agents move in a map and interact with each other through sensors. The decision logic for each agent is written in a subset of Python and the continuous dynamics is given by a black-box simulator. Multiple agents can be instantiated and they can be ported to different maps for creating scenarios. Verse provides functions for simulating and verifying such scenarios using existing reachability analysis algorithms. We illustrate several capabilities and use cases of the library with heterogeneous agents, incremental verification, different sensor models, and the flexibility of plugging in different subroutines for post computations.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2023.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1.
3 papers.
- 2023Refining Perception Contracts: Case Studies in Vision-based Safe Auto-landingcited by 1
- 2025ScenicProver: A Framework for Compositional Probabilistic Verification of Learning-Enabled Systemscited by 1
- 2023Searching for Optimal Runtime Assurance via Reachability and Reinforcement Learningcited by 0
- 2025ScenicProver: A Framework for Compositional Probabilistic Verification of Learning-Enabled Systems4 Nov 2025
- 2023Refining Perception Contracts: Case Studies in Vision-based Safe Auto-landing15 Nov 2023
- 2023Searching for Optimal Runtime Assurance via Reachability and Reinforcement Learning6 Oct 2023