Proximal policy optimization approach to stabilize the chaotic food web system

Abstract

Chaos phenomena can be observed extensively in many real-world scenarios, which usually presents a challenge to suppress those undesired behaviors. Unlike the traditional linear and nonlinear control methods, this study introduces a deep reinforcement learning (DRL)-based scheme to regulate chaotic food web system (FWS). Specifically, we utilize the proximal policy optimization (PPO) algorithm to train the agent model, which does not necessitate the prior knowledge of chaotic FWS. Experimental results demonstrate that the developed DRL-based control scheme can effectively guide the FWS toward a predetermined stable state. Furthermore, this investigation considers the influence of environmental noise on the chaotic FWS, and we obtain the important result that incorporating noise during the training process can enhance the controller’s robustness and system adaptability.