  Quantum computing has a superior advantage in tackling specific problems,
such as integer factorization and Simon's problem. For more general tasks in
machine learning, by applying variational quantum circuits, more and more
quantum algorithms have been proposed recently, especially in supervised
learning and unsupervised learning. However, little work has been done in
reinforcement learning, arguably more important and challenging. Previous work
in quantum reinforcement learning mainly focuses on discrete control tasks
where the action space is discrete. In this work, we develop a quantum
reinforcement learning algorithm based on soft actor-critic -- one of the
state-of-the-art methods for continuous control. Specifically, we use a hybrid
quantum-classical policy network consisting of a variational quantum circuit
and a classical artificial neural network. Tested in a standard reinforcement
learning benchmark, we show that this quantum version of soft actor-critic is
comparable with the original soft actor-critic, using much less adjustable
parameters. Furthermore, we analyze the effect of different hyper-parameters
and policy network architectures, pointing out the importance of architecture
design for quantum reinforcement learning.
