Multistep Q-Learning-Based Optimal Consensus Control of Linear Discrete-Time Multiagent Systems.

Area of Science:

• Control Theory
• Artificial Intelligence
• Multiagent Systems

Background:

• Multiagent systems require robust control strategies to manage complex interactions and environmental uncertainties.
• Balancing exploration and exploitation is crucial for efficient learning in dynamic environments.

Purpose of the Study:

• To develop an optimal consensus control methodology for multiagent systems.
• To enhance learning efficiency by addressing complex dynamics and uncertainty.
• To achieve a balance between exploration and exploitation in agent interactions.

Main Methods:

• Development of the multiagent multistep Q-learning (MaMsQL) algorithm.
• Formulation of the Q-function to establish Nash equilibrium for optimal Q-functions.
• Theoretical proof of convergence for the MaMsQL method.
• Implementation using a specialized Actor-Critic network architecture.

Main Results:

• The Q-function formulation proves that optimal Q-functions constitute a Nash equilibrium.
• The MaMsQL method demonstrates convergence, ensuring stability and effectiveness.
• Simulations confirm the superiority of MaMsQL over multiagent single-step Q-learning.

Conclusions:

• MaMsQL provides an effective solution for optimal consensus control in multiagent systems.
• The methodology successfully handles complex agent interactions and environmental uncertainties.
• The Actor-Critic implementation validates the practical applicability and enhanced performance of MaMsQL.