  Variational Quantum Circuits are being used as versatile Quantum Machine
Learning models. Some empirical results exhibit an advantage in supervised and
generative learning tasks. However, when applied to Reinforcement Learning,
less is known. In this work, we considered a Variational Quantum Circuit
composed of a low-depth hardware-efficient ansatz as the parameterized policy
of a Reinforcement Learning agent. We show that an $\epsilon$-approximation of
the policy gradient can be obtained using a logarithmic number of samples
concerning the total number of parameters. We empirically verify that such
quantum models behave similarly or even outperform typical classical neural
networks used in standard benchmarking environments and in quantum control,
using only a fraction of the parameters. Moreover, we study the Barren Plateau
phenomenon in quantum policy gradients using the Fisher Information Matrix
spectrum.
