mlsanityData to 14 Oct 2026

Domain-driven Metrics for Reinforcement Learning: A Case Study on Epidemic Control using Agent-based Simulation

arXiv 2508.05154 ↗First posted 7 Aug 2025cs.LG cs.AI cs.MAcited by 0

On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 33 references that are in the map. First posted on arXiv 7 Aug 2025.

Builds on

14 of 33 references.

References · 14

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2 more
  1. 2017Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learningcited by 5
  2. 2018Reinforcement Learning and Inverse Reinforcement Learning with System 1 and System 2cited by 4

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  3. 2022Domain Generalization for Robust Model-Based Offline Reinforcement Learning
  4. 2022RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
  5. 2026Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions
  6. 2024Integrating Domain Knowledge for handling Limited Data in Offline RL
  7. 2024Towards a Domain-Specific Modelling Environment for Reinforcement Learning
  8. 2026Reinforcement Learning: From Algorithms To Foundation Models