Project Summary
Approach
Our workflow began with thorough research to build a solid foundational understanding of the problem domain. Once we had a general conceptual framework, we studied relevant open-source implementations to extract architectural insights and best practices. We then proceeded to develop our own system from scratch, dividing responsibilities efficiently: one of us focused on implementing the core system logic, while the other handled model training and reinforcement learning components. After completing the initial prototype, we entered an optimization phase to refine our architecture and improve overall performance.
Challenges
During development, we encountered several technical hurdles, most notably memory leakage and unpredictably long data generation times. These issues significantly impeded our development progress. Nevertheless, through persistent debugging, extensive research from multiple sources, and assistance from peers, we managed to resolve these problems and establish a stable environment for training and evaluation.
Accomplishments
This project marked multiple milestones:
- Our first hands-on experience building an AI system with practical utility.
- First-time use of high-performance computing infrastructure, including supercomputers.
- First foray into training and applying reinforcement learning models in a game-theoretic context.
These accomplishments expanded both our technical skills and our understanding of applied AI systems.
Lessons Learned
We developed a strong grasp of reinforcement learning pipelines, gained conceptual and practical insights into unsupervised learning, and deepened our understanding of game theory as it applies to decision-making in adversarial environments.
Next Steps
Moving forward, we aim to iteratively refine and enhance our current system, codenamed Gigabrain, with the long-term goal of reaching a level of complexity and robustness comparable to DeepStack. Once polished, we intend to open-source the project to share our work with the broader AI and research community.
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