ADP-Based Multi-Agent Consensus Control

ADP-based multi-agent consensus control integrates reinforcement learning with distributed cooperation methodologies, particularly suitable for solving coordination problems among agents in undirected graph structures.

In undirected graph environments, each agent interacts exclusively with neighboring nodes, achieving global state consensus through incremental local information sharing. The core ADP mechanism employs critic networks (for value function estimation) and actor networks (for policy improvement) to learn optimal consensus strategies online, eliminating dependency on explicit system models required by traditional methods. Key advantages include:

Adaptability: Real-time policy adjustments through feedback mechanisms enable dynamic adaptation to topological changes; Low Communication Overhead: Requires only neighbor-node data exchange, making it suitable for large-scale distributed systems; Robustness: Demonstrates strong fault tolerance against noise and nonlinear dynamics.

Implementation requires addressing credit assignment challenges and local observation constraints, typically using consensus error as the reinforcement learning reward signal to drive coordinated optimization among agents. Code implementation often involves neighbor information matrices for local state updates and gradient-based policy iteration algorithms for network weight adjustments.