Multiagent Reinforcement Learning for Active Guidance Control of Railway Vehicles with Independently Rotating Wheels
Juyao Wei,
Zhenggang Lu,
Zheng Yin
et al.
Abstract:This paper presents a novel data-driven multiagent reinforcement learning (MARL) controller for enhancing the running stability of independently rotating wheels (IRW) and reducing wheel–rail wear. We base our active guidance controller on the multiagent deep deterministic policy gradient (MADDPG) algorithm. In this framework, each IRW controller is treated as an independent agent, facilitating localized control of individual wheelsets and reducing the complexity of the required observations. Furthermore, we en… Show more
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