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Multi-agent Continuous Control with Generative Flow Networks.

Shuang Luo1, Yinchuan Li2, Shunyu Liu3

  • 1School of Public Affairs, Zhejiang University, Hangzhou 310027, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Multi-Agent generative Continuous Flow Networks (MACFN) for cooperative exploration in multi-agent systems. MACFN enables decentralized control by decomposing global rewards into local agent contributions, improving exploration capabilities.

Keywords:
Continuous ControlGenerative Flow NetworksMulti-agent System

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Generative Flow Networks (GFlowNets) offer an alternative to reinforcement learning for exploratory control tasks.
  • GFlowNets' individual-flow matching constraint hinders multi-agent system applications, particularly in continuous control.

Purpose of the Study:

  • To propose Multi-Agent generative Continuous Flow Networks (MACFN) for cooperative exploration in multi-agent systems.
  • To enable decentralized control for multiple agents in continuous joint-control problems.

Main Methods:

  • MACFN employs a centralized training approach with decentralized execution.
  • A continuous flow decomposition network deduces individual agent flow contributions from global rewards.
  • Policies are trained in a global-flow-based matching fashion, enabling decentralized action selection based on local flow.

Main Results:

  • MACFN demonstrates superior performance compared to state-of-the-art methods.
  • The proposed method enhances exploration capabilities in multi-agent continuous control tasks.
  • Theoretical derivation of a consistency condition ensures the expressiveness of continuous flow decomposition.

Conclusions:

  • MACFN effectively addresses the limitations of GFlowNets in multi-agent continuous control.
  • The method facilitates cooperative exploration and decentralized control for compositional continuous objects.
  • MACFN provides a promising framework for advanced multi-agent reinforcement learning and exploration.