Quantum Deep Reinforcement Learning for Robot Navigation Tasks

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. For the two encoding schemes, we observed better results for consecutively encoding the classical state vector on each qubit compared to encoding each component on a separate qubit. Our findings demonstrate that current hybrid quantum machine-learning approaches can be scaled to simple robotic problems while yielding sufficient results, at least in an idealized simulated setting, but there are yet open questions regarding the application to considerably more demanding tasks. We anticipate that our work will contribute to introducing quantum machine learning in general and quantum deep reinforcement learning in particular to more demanding problem domains and emphasize the importance of encoding techniques for classic data in hybrid quantum-classical settings.