Research on the Deep Deterministic Policy Algorithm Based on the First-Order Inverted Pendulum

With the mature development of artificial intelligence technology, application intelligent control algorithms in systems has become a trend to meet high-performance requirements modern society. This paper proposes deep deterministic policy gradient (DDPG) controller design method based on reinforcement learning improve system performance. Firstly, optimal DDPG algorithm is derived from Markov decision process and Actor–Critic algorithm. Secondly, order avoid local optima traditional systems, capacity settlement experience pool are adjusted absorb positive accelerate convergence complete efficient training. In response, solve overestimation Q value DDPG, overall structure Critic network changed shorten period at low rates. Finally, first-order inverted pendulum was constructed simulation environment verify effectiveness PID, improved DDPG. The results reveal that faster response disturbances, smaller displacement, angular displacement pendulum. further proves better stability stronger anti-interference ability recovery. provides certain reference for systems.