Can AI agents learn to profit in noisy market simulations?
Deep Reinforcement Learning Agents for Strategic Production Policies in Microeconomic Market Simulations
October 29, 2024
https://arxiv.org/pdf/2410.20550This paper explores using Deep Reinforcement Learning (DRL) to create agents that can learn optimal production strategies within a simulated microeconomic market. This is done to overcome the limitations of traditional economic models that struggle with the complexities and noise present in real-world markets.
The key points relevant to LLM-based multi-agent systems are:
- DRL for Complex Decision-Making: The paper highlights DRL's effectiveness in tackling complex, sequential decision-making problems, making it suitable for simulating agent behavior in dynamic environments like markets.
- Handling Noise and Uncertainty: The research emphasizes DRL's ability to learn adaptive strategies in noisy environments, making it relevant for modeling real-world scenarios where perfect information is unavailable.
- Explainable AI: The authors acknowledge the need for transparency in agent decision-making and suggest using explainable AI techniques to provide insights into the rationale behind the agent's actions.