The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence

Abstract

This paper explores the use of deep reinforcement learning (DRL) to enable autonomous decision-making and strategy optimization in dynamic graphical games. The proposed approach consists of several key components. First, local performance metrics are defined to reduce computational complexity and minimize information exchange among agents. Second, an online iterative algorithm is developed, leveraging Deep Neural Networks to solve dynamic graphical games with input constraints. This algorithm employs an Actor-Critic framework, where the Actor network learns optimal policies and the Critic network estimates value functions. Third, a distributed policy iteration mechanism allows each intelligent agent to make decisions based solely on local information. Finally, experimental results validate the effectiveness of the proposed method. The findings show that the DRL-based online iterative algorithm significantly improves decision accuracy and convergence speed, reduces computational complexity, and demonstrates strong performance and scalability in addressing optimal control problems in dynamic graphical intelligent games.