  This paper presents an equivariant reinforcement learning framework for
quadrotor unmanned aerial vehicles. Successful training of reinforcement
learning often requires numerous interactions with the environments, which
hinders its applicability especially when the available computational resources
are limited, or when there is no reliable simulation model. We identified an
equivariance property of the quadrotor dynamics such that the dimension of the
state required in the training is reduced by one, thereby improving the
sampling efficiency of reinforcement learning substantially. This is
illustrated by numerical examples with popular reinforcement learning
techniques of TD3 and SAC.
