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

arXiv: https://arxiv.org/abs/2508.05154 · HTML: https://mlsanity.com/paper/2508.05154/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 14 of 14)

- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning](https://mlsanity.com/paper/1511.06342/) (2015)
- [Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control](https://mlsanity.com/paper/1708.04133/) (2017)
- [Reinforcement Learning in Economics and Finance](https://mlsanity.com/paper/2003.10014/) (2020)
- [Measuring the Reliability of Reinforcement Learning Algorithms](https://mlsanity.com/paper/1912.05663/) (2019)
- [Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations](https://mlsanity.com/paper/1912.09007/) (2019)
- [Evaluating the Performance of Reinforcement Learning Algorithms](https://mlsanity.com/paper/2006.16958/) (2020)
- [Metrics and continuity in reinforcement learning](https://mlsanity.com/paper/2102.01514/) (2021)
- [Comparison of Reinforcement Learning algorithms applied to the Cart Pole problem](https://mlsanity.com/paper/1810.01940/) (2018)
- [Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning](https://mlsanity.com/paper/1710.10363/) (2017)
- [Reinforcement Learning and Inverse Reinforcement Learning with System 1 and System 2](https://mlsanity.com/paper/1811.08549/) (2018)

## 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.