  In recent years, near-term noisy intermediate scale quantum (NISQ) computing
devices have become available. One of the most promising application areas to
leverage such NISQ quantum computer prototypes is quantum machine learning.
While quantum neural networks are widely studied for supervised learning,
quantum reinforcement learning is still just an emerging field of this area. To
solve a classical continuous control problem, we use a continuous-variable
quantum machine learning approach. We introduce proximal policy optimization
for photonic variational quantum agents and also study the effect of the data
re-uploading. We present performance assessment via empirical study using
Strawberry Fields, a photonic simulator Fock backend and a hybrid training
framework connected to an OpenAI Gym environment and TensorFlow. For the
restricted CartPole problem, the two variations of the photonic policy learning
achieve comparable performance levels and a faster convergence than the
baseline classical neural network of same number of trainable parameters.
