Design of zero-sum differential game control based on model-free reinforcement learning method and disturbance observer

Key Points

• The zero-sum differential game control system optimizes strategies without model dependency, demonstrating flexibility.
• Key evidence shows significant performance improvements using a model-free reinforcement learning method.
• Observational analysis employed a disturbance observer to stabilize control in dynamic environments across multiple scenarios.
• These findings suggest that integrating reinforcement learning may enhance decision-making in complex control applications.