Deep Reinforcement Learning Agents for Strategic Production Policies in Microeconomic Market Simulations
On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance
Abstract (arXiv)
Traditional economic models often rely on fixed assumptions about market dynamics, limiting their ability to capture the complexities and stochastic nature of real-world scenarios. However, reality is more complex and includes noise, making traditional models assumptions not met in the market. In this paper, we explore the application of deep reinforcement learning (DRL) to obtain optimal production strategies in microeconomic market environments to overcome the limitations of traditional models. Concretely, we propose a DRL-based approach to obtain an effective policy in competitive markets with multiple producers, each optimizing their production decisions in response to fluctuating demand, supply, prices, subsidies, fixed costs, total production curve, elasticities and other effects contaminated by noise. Our framework enables agents to learn adaptive production policies to several simulations that consistently outperform static and random strategies. As the deep neural networks used by the agents are universal approximators of functions, DRL algorithms can represent in the network complex patterns of data learnt by trial and error that explain the market. Through extensive simulations, we demonstrate how DRL can capture the intricate interplay between production costs, market prices, and competitor behavior, providing insights into optimal decision-making in dynamic economic settings. The results show that agents trained with DRL can strategically adjust production levels to maximize long-term profitability, even in the face of volatile market conditions. We believe that the study bridges the gap between theoretical economic modeling and practical market simulation, illustrating the potential of DRL to revolutionize decision-making in market strategies.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 21 references that are in the map. First posted on arXiv 27 Oct 2024.
Builds on
10 of 21 references.
Closest ancestors · 1
Same direction, newest first
- 2024Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent19 Jul
Other references · 9
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
- 2018An Introduction to Deep Reinforcement Learningcited by 206
- 2021Recent Advances in Reinforcement Learning in Financecited by 110
- 2020Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economicscited by 23
- 2022Automated Reinforcement Learning: An Overviewcited by 9
- 2023Deep Reinforcement Learning for ESG financial portfolio managementcited by 2
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