Data-Based Optimal Synchronization of Heterogeneous Multiagent Systems in Graphical Games via Reinforcement Learning

Area of Science:

• Control Theory
• Artificial Intelligence
• Systems Engineering

Background:

• Multiagent systems (MASs) require robust synchronization strategies, especially when system dynamics are partially unknown.
• Achieving optimal control and minimizing performance indices are critical challenges in MASs.
• Graphical game frameworks offer a structured approach to analyzing agent interactions and control policies.

Purpose of the Study:

• To develop a framework for optimal synchronization of linear heterogeneous MASs with partial system uncertainty.
• To design algorithms that achieve system synchronization and minimize individual agent performance indices.
• To validate the proposed methods through theoretical analysis and numerical simulations.

Main Methods:

• Formulation of a heterogeneous multiagent graphical game framework.
• Development of a model-based policy iteration (PI) algorithm to solve the Hamilton-Jacobian-Bellmen (HJB) equation.
• Introduction of a data-based off-policy integral reinforcement learning (IRL) algorithm using a single-critic neural network (NN).
• Application of gradient descent for training NNs based on collected behavioral data.

Main Results:

• Proof that the optimal control policy derived from the HJB equation represents a Nash equilibrium and is a best response.
• Demonstration of the convergence of the proposed model-based PI and data-based IRL algorithms.
• Validation that the NN weight-tuning law facilitates optimal synchronization.
• Numerical example confirming the effectiveness of the theoretical findings.

Conclusions:

• The proposed data-based off-policy IRL algorithm effectively achieves optimal synchronization in linear heterogeneous MASs with unknown dynamics.
• The single-critic neural network implementation provides a practical approach for complex control problems.
• The theoretical framework and algorithms offer a robust solution for enhancing MAS performance and stability.