  Recent advances in quantum computing (QC) and machine learning (ML) have
drawn significant attention to the development of quantum machine learning
(QML). Reinforcement learning (RL) is one of the ML paradigms which can be used
to solve complex sequential decision making problems. Classical RL has been
shown to be capable to solve various challenging tasks. However, RL algorithms
in the quantum world are still in their infancy. One of the challenges yet to
solve is how to train quantum RL in the partially observable environments. In
this paper, we approach this challenge through building QRL agents with quantum
recurrent neural networks (QRNN). Specifically, we choose the quantum long
short-term memory (QLSTM) to be the core of the QRL agent and train the whole
model with deep $Q$-learning. We demonstrate the results via numerical
simulations that the QLSTM-DRQN can solve standard benchmark such as Cart-Pole
with more stable and higher average scores than classical DRQN with similar
architecture and number of model parameters.
