<p>This paper extends the Differential Dynamic Programming (DDP) approach to nonzero-sum differential games that solve multi-agent control problems where each individual player has its own objective. The traditional DDP approach is an optimal control algorithm that finds the control to optimize the performance of a single player iteratively. However, many real-world systems involve competing agents with different goals and require a nonzero-sum framework. We develop a DDP-based methodology that computes optimal feedback strategies for each player while accounting for the interactions between multiple agents. Additionally, this work introduces a generalized extension to Min-Max differential games and offers a robust approach for handling disturbances in multi-agent settings where the actions of other players are treated as intelligent disturbances. Simulations are presented to demonstrate the effectiveness of the approach in scenarios involving both cooperative and adversarial agents, highlighting its potential in applications on dynamic multi-agent systems.</p>