The STP-based Stackelberg game solver has certain limitations that can be addressed for further improvement:
Limited Cost Functions: The current solver may be limited in handling complex cost functions beyond the ones considered in the existing framework. To improve this, the solver can be extended to accommodate a wider range of cost functions, including non-convex and nonlinear terms.
Constraint Handling: The solver may struggle with handling a large number of constraints or complex constraint structures. Enhancements can be made to efficiently handle various types of constraints, such as path constraints, terminal constraints, and state constraints.
Scalability: The scalability of the solver may be a concern when dealing with a large number of agents or a high-dimensional state space. Improvements in algorithm efficiency and parallel computing can help enhance scalability.
Real-time Adaptability: The solver may need enhancements to adapt in real-time to dynamic environments and changing conditions. This can involve developing adaptive strategies and online learning mechanisms.
Integration of Learning: Incorporating machine learning techniques can improve the solver's performance by learning from data and optimizing strategies based on past experiences.
By addressing these limitations, the STP-based Stackelberg game solver can become more versatile, robust, and applicable to a wider range of multi-agent coordination problems.