  Quantum reinforcement learning utilizes quantum layers to process information
within a machine learning model. However, both pure and hybrid quantum
reinforcement learning face challenges such as data encoding and the use of
quantum computers during the inference stage. We apply the Quantum-Train method
to reinforcement learning tasks, called QTRL, training the classical policy
network model using a quantum machine learning model with polylogarithmic
parameter reduction. This QTRL approach eliminates the data encoding issues of
conventional quantum machine learning and reduces the training parameters of
the corresponding classical policy network. Most importantly, the training
result of the QTRL is a classical model, meaning the inference stage only
requires classical computer. This is extremely practical and cost-efficient for
reinforcement learning tasks, where low-latency feedback from the policy model
is essential.
