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.
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.