Two-Stage Homotopic Learning for Data-Driven Cluster Consensus in Multiagent Systems

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Game Theory

Background:

• Distributed cluster consensus is crucial for coordinated behavior in multiagent systems (MASs).
• Existing methods often require known system models or admissible initial policies, limiting their applicability.
• Continuous-time MASs present unique challenges for achieving stable and efficient consensus.

Purpose of the Study:

• To propose a novel homotopic reinforcement learning (RL) framework for distributed cluster consensus in continuous-time MASs.
• To formulate the consensus problem as a zero-sum differential game for optimal control.
• To develop a data-driven approach that overcomes the need for known system models and admissible initial policies.

Main Methods:

• Formulation of the consensus problem as a zero-sum differential game with a minmax game policy.
• Derivation of group game algebraic Riccati equations for the optimal control law.
• Application of a data-driven homotopic policy-iteration scheme using online state and input information.

Main Results:

• The proposed homotopic RL framework effectively addresses the distributed cluster consensus problem.
• The data-driven approach successfully solves the derived algebraic Riccati equations without prior model knowledge.
• Stability and convergence are rigorously proven, demonstrating the method's reliability.

Conclusions:

• The novel homotopic RL framework offers a robust and data-driven solution for distributed cluster consensus in MASs.
• The formulation as a zero-sum differential game provides a new perspective for tackling consensus problems.
• The method's ability to relax initial policy requirements enhances its practical applicability in complex systems.