# DiCriTest: Testing Scenario Generation for Decision-Making Agents Considering Diversity and Criticality

arXiv: https://arxiv.org/abs/2508.11514 · HTML: https://mlsanity.com/paper/2508.11514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics](https://mlsanity.com/paper/2005.04269/) (2020)
- [DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems](https://mlsanity.com/paper/1803.07519/) (2018)
- [A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective](https://mlsanity.com/paper/2202.02215/) (2022)
- ["Curse of rarity" for autonomous vehicles](https://mlsanity.com/paper/2207.02749/) (2022)
- [Scenarios for Development, Test and Validation of Automated Vehicles](https://mlsanity.com/paper/1801.08598/) (2018)
- [Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.11084/) (2023)
- [Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning](https://mlsanity.com/paper/1805.00200/) (2018)
- [Deep Reinforcement Learning-based Large-scale Robot Exploration](https://mlsanity.com/paper/2403.10833/) (2024)
- [MDPFuzz: Testing Models Solving Markov Decision Processes](https://mlsanity.com/paper/2112.02807/) (2021)
- [Sim-to-Real Surgical Robot Learning and Autonomous Planning for Internal Tissue Points Manipulation using Reinforcement Learning](https://mlsanity.com/paper/2306.14085/) (2023)
- [Enhancing Autonomous Driving Safety with Collision Scenario Integration](https://mlsanity.com/paper/2503.03957/) (2025)
- [Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees](https://mlsanity.com/paper/2110.10729/) (2021)
- [Fast Falsification of Hybrid Systems using Probabilistically Adaptive Input](https://mlsanity.com/paper/1812.04159/) (2018)
- [Policy Testing with MDPFuzz (Replicability Study)](https://mlsanity.com/paper/2502.19116/) (2025)
- [Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems](https://mlsanity.com/paper/2403.13869/) (2024)
- [Testing for Fault Diversity in Reinforcement Learning](https://mlsanity.com/paper/2403.15065/) (2024)

## Built on by (most cited first, 1 of 1)

- [NeSy-CSA: A Neuro-Symbolic Framework for Open-Ended Critical Scenario Attribution](https://mlsanity.com/paper/2607.03847/) (2026)

## Abstract (arXiv)

The growing deployment of decision-making agents in dynamic environments increases the demand for safety verification. While critical testing scenario generation has emerged as an appealing verification methodology, effectively balancing diversity and criticality remains a key challenge for existing methods, particularly due to local optima entrapment in high-dimensional scenario spaces. To address this limitation, we propose a dual-space guided testing framework that coordinates scenario parameter space and agent behavior space, aiming to generate testing scenarios considering diversity and criticality. Specifically, in the scenario parameter space, a hierarchical representation framework combines dimensionality reduction and multi-dimensional subspace evaluation to efficiently localize diverse and critical subspaces. This guides dynamic coordination between two generation modes: local perturbation and global exploration, optimizing critical scenario quantity and diversity. Complementarily, in the agent behavior space, agent-environment interaction data are leveraged to quantify behavioral criticality/diversity and adaptively support generation mode switching, forming a closed feedback loop that continuously enhances scenario characterization and exploration within the parameter space. Experiments show our framework improves critical scenario generation by an average of 56.23% and demonstrates greater diversity under novel parameter-behavior co-driven metrics when tested on five decision-making agents, outperforming state-of-the-art baselines.