Reinforcement Learning Position Control of a Quadrotor Using Soft Actor-Critic (SAC)

arXiv:2512.18333v2 Announce Type: replace-cross Abstract: This paper proposes a new Reinforcement Learning (RL) based control architecture for quadrotors. With the literature focusing on controlling the four rotors' RPMs directly, this paper aims to control the quadrotor's thrust vector. The RL agent computes the percentage of overall thrust along the quadrotor's z-axis along with the desired Roll ($\phi$) and Pitch ($\theta$) angles. The agent then sends the calculated control signals along with the current quadrotor's Yaw angle ($\psi$) to an attitude PID controller. The PID controller then

Source: arXiv cs.LG — read the full report at the original publisher.

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