MetaMathAgent is a prototype exploring the architecture of an autonomous multi-agent system. It simulates a system capable of generating problems, reasoning through them, and learning from feedback using reinforcement learning, symbolic reasoning, and cognitive modeling.

Project Overview

The agent comprises modular components that interact to emulate a reasoning process:

• Problem Generator: Creates mathematical problems for the agent to solve.
• Reasoning Agent: Applies reasoning strategies to tackle generated problems.
• Feedback Generator: Provides feedback based on the agent's performance.
• Episodic Memory: Stores experiences to inform future reasoning.
• Integration Controller: Coordinates interactions among components.
• Reinforcement Learning System: Adjusts strategies based on feedback.