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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.05154 |
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| _version_ | 1866912526896201728 |
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| author | Gaur, Rishabh Deshkar, Gaurav Kshirsagar, Jayanta Hayatnagarkar, Harshal Venugopalan, Janani |
| author_facet | Gaur, Rishabh Deshkar, Gaurav Kshirsagar, Jayanta Hayatnagarkar, Harshal Venugopalan, Janani |
| contents | For the development and optimization of agent-based models (ABMs) and rational agent-based models (RABMs), optimization algorithms such as reinforcement learning are extensively used. However, assessing the performance of RL-based ABMs and RABMS models is challenging due to the complexity and stochasticity of the modeled systems, and the lack of well-standardized metrics for comparing RL algorithms. In this study, we are developing domain-driven metrics for RL, while building on state-of-the-art metrics. We demonstrate our ``Domain-driven-RL-metrics'' using policy optimization on a rational ABM disease modeling case study to model masking behavior, vaccination, and lockdown in a pandemic. Our results show the use of domain-driven rewards in conjunction with traditional and state-of-the-art metrics for a few different simulation scenarios such as the differential availability of masks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05154 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain-driven Metrics for Reinforcement Learning: A Case Study on Epidemic Control using Agent-based Simulation Gaur, Rishabh Deshkar, Gaurav Kshirsagar, Jayanta Hayatnagarkar, Harshal Venugopalan, Janani Machine Learning Artificial Intelligence Multiagent Systems For the development and optimization of agent-based models (ABMs) and rational agent-based models (RABMs), optimization algorithms such as reinforcement learning are extensively used. However, assessing the performance of RL-based ABMs and RABMS models is challenging due to the complexity and stochasticity of the modeled systems, and the lack of well-standardized metrics for comparing RL algorithms. In this study, we are developing domain-driven metrics for RL, while building on state-of-the-art metrics. We demonstrate our ``Domain-driven-RL-metrics'' using policy optimization on a rational ABM disease modeling case study to model masking behavior, vaccination, and lockdown in a pandemic. Our results show the use of domain-driven rewards in conjunction with traditional and state-of-the-art metrics for a few different simulation scenarios such as the differential availability of masks. |
| title | Domain-driven Metrics for Reinforcement Learning: A Case Study on Epidemic Control using Agent-based Simulation |
| topic | Machine Learning Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2508.05154 |