  We utilize hybrid quantum deep reinforcement learning to learn navigation
tasks for a simple, wheeled robot in simulated environments of increasing
complexity. For this, we train parameterized quantum circuits (PQCs) with two
different encoding strategies in a hybrid quantum-classical setup as well as a
classical neural network baseline with the double deep Q network (DDQN)
reinforcement learning algorithm. Quantum deep reinforcement learning (QDRL)
has previously been studied in several relatively simple benchmark
environments, mainly from the OpenAI gym suite. However, scaling behavior and
applicability of QDRL to more demanding tasks closer to real-world problems e.
g., from the robotics domain, have not been studied previously. Here, we show
that quantum circuits in hybrid quantum-classic reinforcement learning setups
are capable of learning optimal policies in multiple robotic navigation
scenarios with notably fewer trainable parameters compared to a classical
baseline. Across a large number of experimental configurations, we find that
the employed quantum circuits outperform the classical neural network baselines
when equating for the number of trainable parameters. Yet, the classical neural
network consistently showed better results concerning training times and
stability, with at least one order of magnitude of trainable parameters more
than the best-performing quantum circuits. However, validating the robustness
of the learning methods in a large and dynamic environment, we find that the
classical baseline produces more stable and better performing policies overall.
