标题： 一种用于玩Atari游戏的量子-经典强化学习模型 显示英文标题

标题： A quantum-classical reinforcement learning model to play Atari games

摘要： 强化学习的最新进展表明，基于参数化量子电路的量子学习模型作为深度学习模型的替代方案具有潜力。 一方面，这些研究显示了在某些——人为构建的——环境中，全功能量子模型可以提供的终极指数级加速。 另一方面，它们展示了实验上可访问的PQC解决OpenAI Gym基准任务的能力。 然而，这些近期的QRL技术是否能成功应用于具有高维观察空间的更复杂问题，仍然是一个开放性问题。 在这项工作中，我们填补了这一空白，并提出了一种混合模型，该模型结合了PQC与经典特征编码和后处理层，能够处理Atari游戏。 构建了一个受到与混合模型中类似的架构限制的经典模型，用作参考。 我们的数值研究证明，所提出的混合模型能够解决Pong环境，并在Breakout中取得与经典参考相当的分数。 此外，我们的研究结果揭示了影响量子和经典组件相互作用的重要超参数设置和设计选择。 这项工作有助于理解近期量子学习模型，并朝着它们在现实世界RL场景中的部署迈出了重要一步。 显示英文摘要

摘要： Recent advances in reinforcement learning have demonstrated the potential of quantum learning models based on parametrized quantum circuits as an alternative to deep learning models. On the one hand, these findings have shown the ultimate exponential speed-ups in learning that full-blown quantum models can offer in certain -- artificially constructed -- environments. On the other hand, they have demonstrated the ability of experimentally accessible PQCs to solve OpenAI Gym benchmarking tasks. However, it remains an open question whether these near-term QRL techniques can be successfully applied to more complex problems exhibiting high-dimensional observation spaces. In this work, we bridge this gap and present a hybrid model combining a PQC with classical feature encoding and post-processing layers that is capable of tackling Atari games. A classical model, subjected to architectural restrictions similar to those present in the hybrid model is constructed to serve as a reference. Our numerical investigation demonstrates that the proposed hybrid model is capable of solving the Pong environment and achieving scores comparable to the classical reference in Breakout. Furthermore, our findings shed light on important hyperparameter settings and design choices that impact the interplay of the quantum and classical components. This work contributes to the understanding of near-term quantum learning models and makes an important step towards their deployment in real-world RL scenarios.