  Deep reinforcement learning (DRL) often requires a large number of data and
environment interactions, making the training process time-consuming. This
challenge is further exacerbated in the case of batch RL, where the agent is
trained solely on a pre-collected dataset without environment interactions.
Recent advancements in quantum computing suggest that quantum models might
require less data for training compared to classical methods. In this paper, we
investigate this potential advantage by proposing a batch RL algorithm that
utilizes VQC as function approximators within the discrete batch-constraint
deep Q-learning (BCQ) algorithm. Additionally, we introduce a novel data
re-uploading scheme by cyclically shifting the order of input variables in the
data encoding layers. We evaluate the efficiency of our algorithm on the OpenAI
CartPole environment and compare its performance to the classical neural
network-based discrete BCQ.
