Decentralized Multi-Agent Control via Neural Network Segmentation
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Solution Overview
Problem
Large-scale multi-agent systems face challenges such as the 'curse of dimensionality,' limited communication capabilities, and physical system constraints, making conventional control techniques difficult to apply effectively.
Innovation Solution
A decentralized optimal control method using three neural networks (actor NN, critic NN, and mass NN) with adaptive dynamic programming and reinforcement learning to solve coupled Hamiltonian-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations, integrating boundary and density constraints through a barrier function-based system transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cooperative control is applied to large-scale multi-agent systems, then control performance can be improved, but computational complexity increases exponentially due to the curse of dimensionality
Solution Approach 1:
The patent segments the control problem by introducing individual agent controllers that operate independently based on local information, rather than requiring global coordination. Each agent uses its own controller with neural networks to make decisions, dividing the complex system-wide control into simpler individual control units, thereby reducing computational complexity while maintaining control performance.
Solution Approach 2:
The patent introduces communication protocols and information exchange mechanisms as intermediaries between agents. Instead of direct complex coordination, agents exchange simplified information through defined communication channels, which mediates the interaction and reduces the computational burden of direct cooperative control while maintaining system reliability.
2Adaptability or versatility
If distributed control techniques are used to handle large-scale systems, then system scalability is improved, but reliability decreases due to limited and unreliable communication networks
Solution Approach 1:
The patent implements dynamic adaptation in the control system, where agents can adjust their control strategies and communication behavior based on real-time system conditions. The controller dynamically modifies its operation based on available information and communication quality, allowing the system to maintain scalability while adapting to communication reliability variations.
Solution Approach 2:
The patent incorporates feedback mechanisms where agents receive information about system state and communication quality, and adjust their control actions accordingly. This feedback loop allows agents to compensate for communication limitations and maintain reliable operation in scalable distributed systems despite network constraints.
3Manufacturing precision
If optimal control design is applied to large-scale multi-agent systems, then control precision is improved, but implementation difficulty increases due to physical system limitations and environmental constraints
Solution Approach 1:
The patent changes the parameters and formulation of the control problem to make it more suitable for large-scale systems. By reformulating the optimal control problem with constraints from physical systems and environment incorporated into the control objective function, the system achieves high control precision while reducing implementation difficulty through simplified constraint handling.
Data Source
AI summary
The present disclosure provides a method, a device, and a storage medium for decentralized optimal control for a large-scale multi-agent system. The method includes initializing errors to obtain an initialized error of each of an actor NN, a critic NN and a mass NN; initializing error thresholds to obtain an initialized error threshold of each of the actor NN, the critic NN and the mass NN; and if the initialized error of each of the actor NN, the critic NN and the mass NN is greater than or equal to a corresponding initialized error threshold, calculating weights of each of the actor NN, the critic NN and the mass NN, and updating the actor NN, the critic NN, and the mass NN; and calculating errors of each of the actor NN, the critic NN and the mass NN, and updating the actor NN, the critic NN, and the mass NN.