Reinforcement Learning for Motor Control: A Comprehensive Review
On the map: Deep reinforcement learning methods → Deep RL algorithms and representations
Abstract (arXiv)
Electric motors are crucial in many applications, but traditional control methods struggle with nonlinearities, parameter uncertainties, and external disturbances. Reinforcement Learning (RL) offers a promising solution as a data-driven approach that can learn optimal control strategies without an explicit model. This review paper examines the current state of RL in motor control, exploring various RL algorithms and applications. The review highlights RL's advantages, including model-free control, adaptability to changing conditions, and the ability to optimize for complex objectives. It also addresses challenges in applying RL to motor control, such as sim-to-real transfer, safety and stability concerns, scalability, and computational complexity. By providing a comprehensive overview of the field, this review aims to deepen understanding of RL's potential to revolutionize motor control and drive advancements across industries.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 0 of its 19 references that are in the map. First posted on arXiv 23 Dec 2024.
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