What are the differences between centralized learning (in monolithic systems) and decentralized learning (in multi-agent systems)?

Summary

  • Question: Why are we studying social learning in multi-agent systems?
  • Answer: Multi-agent systems are made up of agents where each has its own objective. We believe that this leads to learning dynamics that are impossible in centralized systems.
  • Monolithic system (centralized): has one objective that is shared by all parts of the system. Let’s assume a homogeneous network and learning via a back-prop algorithm. This is what most deep learning is about.
  • Multi-agent system (decentralized): each part of the system has its own objective, therefore its learning resembles social learning in an evolving population of agents. We are not assuming back-prop learning.

Example of collective learning in a multi-agent system

Monolithic vs Multi-agent

Conclusion

Questions for reflection

  • External information storage — Is it the key for better collective learning? The storage can be cumulative and bigger than the memory of an individual agent.
  • Multiple feedback mechanisms — A social system can have many adaptive feedback mechanisms, will they scale better than a centralized one in monolithic systems?
  • Efficiency threshold — Is there a threshold at which social systems become more efficient than monolithic systems?
  • Better scaling — does a modular / hierarchical system with mostly local communication scale better than a monolithic system?
  • Replication of skills — discovered skills can be replicated to other parts of the society, whereas in a monolithic system it needs to be rediscovered. Are there some counterexamples?
  • Open-ended learning — due to not having a single fixed feedback mechanism and the learning diverges, the social systems are more suitable for open-ended learning.
  • Does a monolithic system learn faster than a multi-agent system?
  • Is there a limit where a monolithic system won’t be sufficient anymore and you need to switch to multi-agent learning? Can we get open-ended learning inside a monolithic system?
  • Can we simulate a multi-agent system on a monolithic system ? For example, a multi-agent system being simulated by a monolithic interpreter.

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