Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
Abstract
This paper introduces a novel reinforcement learning framework for robot control, leveraging adaptive reinforcement learning (ARL) to dynamically adjust control strategies based on both environmental feedback and the robot's internal state. Traditional reinforcement learning models often suffer from static policies, limiting their ability to effectively handle dynamic environments. This proposed method addresses this limitation by employing ARL to continuously refine control decisions, resulting in enhanced robot autonomy and adaptability. The core mechanism centers on the iterative adjustment of control parameters through the reinforcement learning process, allowing the robot to respond to evolving conditions in a flexible and intelligent manner. This work aims to provide a more robust and adaptable approach to robot control compared to traditional methods.
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