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Proximal policy optimization approach to stabilize the chaotic food web system

Author

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  • Xu, Liang
  • Ma, Ru-Ru
  • Wu, Jie
  • Rao, Pengchun

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.

Suggested Citation

  • Xu, Liang & Ma, Ru-Ru & Wu, Jie & Rao, Pengchun, 2025. "Proximal policy optimization approach to stabilize the chaotic food web system," Chaos, Solitons & Fractals, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:chsofr:v:192:y:2025:i:c:s0960077925000463
    DOI: 10.1016/j.chaos.2025.116033
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    References listed on IDEAS

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    1. Wu, Jie & Xu, Wei & Wang, Xiaofeng & Ma, Ru-ru, 2021. "Stochastic adaptive fixed-time stabilization of chaotic systems with applications in PMSM and FWS," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    2. Cheng, Haoxin & Li, Haihong & Dai, Qionglin & Yang, Junzhong, 2023. "A deep reinforcement learning method to control chaos synchronization between two identical chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    4. Chen, Wei-Ching, 2008. "Nonlinear dynamics and chaos in a fractional-order financial system," Chaos, Solitons & Fractals, Elsevier, vol. 36(5), pages 1305-1314.
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    Cited by:

    1. Li, Xintong & Zhao, Judi & Zhang, Yinxing, 2025. "Generation of one-dimensional complex discrete hyperchaotic maps with hardware implementation," Chaos, Solitons & Fractals, Elsevier, vol. 200(P1).
    2. Zhong, Xiaojing & Liang, Kunkai & Deng, Feiqi & Zhao, Xueyan, 2025. "A control approach for traffic congestion based on multipath propagation model," Chaos, Solitons & Fractals, Elsevier, vol. 199(P1).

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