𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗦𝗲𝗹𝗳-𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
Multi-agent systems (MAS) solve complex problems through agent collaboration. Most systems follow fixed rules. You can build a system where agents learn and adapt on their own.
A self-evolving MAS uses reinforcement learning to improve behavior over time. These agents do three things:
- Operate in a shared environment.
- Learn from successes and failures.
- Update policies based on collective experience.
You can build this using three technologies:
- Python asyncio for running agents at the same time.
- Q-learning for agent adaptation.
- Shared memory for knowledge sharing.
The architecture has four parts:
- Environment: A grid world where agents find rewards.
- Agents: Independent entities with Q-tables.
- Coordinator: Manages agent life and experience.
- Evolution Engine: Selects and mutates the best agents.
The Evolution Engine handles the growth. It sorts agents by fitness. It picks the top performers and creates a new generation. It uses mutation to introduce new strategies. This allows the system to optimize itself without manual help.
This approach creates software that improves through experience.
Source: https://dev.to/biao_lin_14b493a4944b1361/building-a-self-evolving-multi-agent-system-with-python-8b0
Optional learning community: https://t.me/GyaanSetuAi