Project AA

Inspiration

Traditional AI agents often get stuck in repetitive, safe routines (local optima) when exploring open-world environments. I wanted to design an agent that naturally seeks the absolute fastest, most efficient path to complete a goal without human intervention—simulating "speedrun" behavior.

What it does

Project AA is a generalized AI Agent powered by Reinforcement Learning. It parses digital interfaces dynamically and uses a "Continuous Negative Reward" system. By docking points for every passing second, the AI is under constant pressure to cut out redundant actions and rush toward core milestones.

How we built it

The core architecture is built using Python and Reinforcement Learning principles. We planned the initial sandbox MVP training inside Minecraft using computer vision tools to dynamically parse the environment and track reward milestones.

Challenges we faced

Designing a balanced reward/penalty matrix is incredibly tough. If the time-decay penalty is too low, the AI idles; if it's too high, the AI becomes too discouraged to explore. Fine-tuning this pressure is our biggest engineering challenge.

What's next for Project AA

We aim to fully deploy the sandbox MVP in Minecraft. Once perfected, we want to use transfer learning to extract this core engine and apply it to universal web interfaces for complex enterprise automation.

Built With

• minerl
• opencv
• python
• reinforcement-learning