Reinforcement learning-based control for robots has been a hot research topic in recent years, as it has demonstrated potential advantages over traditional control methods for nonlinear systems, including reduced reliance on model information and improved adaptability to model uncertainty and changes in control objectives. The double pendulum system, with its highly nonlinear dynamical model and chaotic behavior, serves as an ideal platform for developing new controllers and algorithms. This thesis aims to applya novel reinforcement learning-based control policyon the double pendulum systemthat achieves both swing-up and stabilization tasks. The proposed control policywill first be validated in a simulation environment, with subsequent attempts to deploy it on the robot by directly training the agent on hardware to address the sim2real gap problem.
Reinforcement Learning-based Control for Swing-up and Stabilization of an Underactuated Double Pendulum System
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