This repository explores an innovative adaptation of the Reflexion-inspired reinforcement learning (RL) model to the Bertrand price competition model. This project takes a novel approach compared to traditional RL methods, aiming to deepen the understanding of tacit collusion and market dynamics in economic models.
- Cyclical Process: Implements a cyclical process of action generation, market response evaluation, and reflection on outcomes to inform future strategies.
- Strategic Depth and Adaptability: Adds strategic depth and adaptability, offering a new dimension to economic modeling.
- Trajectory Learning and Complex Evaluation: Focuses on learning from entire action trajectories and conducting complex evaluations of market responses.
- Long-Term Strategy Development: Provides insights into long-term strategy development in dynamic market environments.
Building on traditional economic modeling and RL, this project integrates advanced AI methodologies to explore new aspects of the Bertrand price competition model. It aims to offer fresh perspectives on competitive strategies and collusion scenarios.
For detailed information on the objectives, methodology, and background of this project, refer to the Research Proposal.
We welcome contributions to this project, including feature enhancements, bug fixes, and documentation improvements. Please feel free to fork the repository and submit pull requests.
This project is licensed under [license name]. See the LICENSE.md file for details.