mlsanityData to 14 Oct 2026

Reward Engineering for Spatial Epidemic Simulations: A Reinforcement Learning Platform for Individual Behavioral Learning

arXiv 2511.18000 ↗First posted 22 Nov 2025cs.LG cs.AI q-bio.PEcited by 0

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

Abstract (arXiv)

We present ContagionRL, a Gymnasium-compatible reinforcement learning platform specifically designed for systematic reward engineering in spatial epidemic simulations. Unlike traditional agent-based models that rely on fixed behavioral rules, our platform enables rigorous evaluation of how reward function design affects learned survival strategies across diverse epidemic scenarios. ContagionRL integrates a spatial SIRS+D epidemiological model with configurable environmental parameters, allowing researchers to stress-test reward functions under varying conditions including limited observability, different movement patterns, and heterogeneous population dynamics. We evaluate five distinct reward designs, ranging from sparse survival bonuses to a novel potential field approach, across multiple RL algorithms (PPO, SAC, A2C). Through systematic ablation studies, we identify that directional guidance and explicit adherence incentives are critical components for robust policy learning. Our comprehensive evaluation across varying infection rates, grid sizes, visibility constraints, and movement patterns reveals that reward function choice dramatically impacts agent behavior and survival outcomes. Agents trained with our potential field reward consistently achieve superior performance, learning maximal adherence to non-pharmaceutical interventions while developing sophisticated spatial avoidance strategies. The platform's modular design enables systematic exploration of reward-behavior relationships, addressing a knowledge gap in models of this type where reward engineering has received limited attention. ContagionRL is an effective platform for studying adaptive behavioral responses in epidemic contexts and highlight the importance of reward design, information structure, and environmental predictability in learning. Our code is publicly available at https://github.com/redradman/ContagionRL

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 21 of its 69 references that are in the map. First posted on arXiv 22 Nov 2025.

Builds on

21 of 69 references.

References · 21

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9 more
  1. 2020Deep reinforcement learning for large-scale epidemic controlcited by 16
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  1. 2026Graph World Models for Constrained Epidemic Policy Planning
  2. 2026medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions
  3. 2024Model-Based Reinforcement Learning Control of Reaction-Diffusion Problems
  4. 2026Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control
  5. 2026Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions
  6. 2026Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning
  7. 2025Reinforcement Learning for Large Model: A Survey
  8. 2024Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms