Reinforcement Learning-Enabled Investment ABM in NetLogo Available on CoMSES
Muhammad Khurram Ali
Dear NetLogo Community,
I would like to share our peer-reviewed NetLogo model recently published in the CoMSES Computational Model Library:
"Integrating Reinforcement Learning in Agent-Based Modeling for Dynamic Investment Decisions"
https://doi.org/10.25937/644j-cv09
The model extends the investment model introduced by Volker Grimm and Steven F. Railsback by embedding Reinforcement Learning, specifically Proximal Policy Optimization (PPO), into investor agent behavior.
Instead of following fixed decision rules, investor agents learn from their interactions with a dynamic economic environment and continuously adapt their investment strategies. In our experiments, PPO-enabled agents demonstrated improved wealth accumulation, lower risk exposure, fewer business failures, and a larger number of flourishing opportunities compared with earlier model variants.
This model is part of a broader research effort on reinforcement learning-enabled agent-based modeling and adaptive simulation systems. A companion study currently under review compares Q-Learning, Deep Q Networks (DQN), and PPO within the same modeling framework using extensive NetLogo BehaviorSpace experiments, mathematical modeling, and ANN-based recommendation capabilities.
The model is openly available, and I would welcome any feedback, suggestions, or collaborations from the community.
I would be particularly interested in hearing from researchers who have integrated reinforcement learning with NetLogo models, explored learning agents in ABM, or are working toward adaptive simulation systems and intelligent digital twins.
Many thanks to my co-authors, Haider Ali and Hafiz Mohammad, whose hard work were instrumental in bringing this project to completion.
Best regards,
Muhammad Khurram Ali
Associate Professor
Department of Industrial Engineering
University of Engineering and Technology Taxila, Pakistan
LinkedIn: www.linkedin.com/in/muhammad-khurram-ali-ied