This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks. The framework incorporates prioritized experience replay and asynchronous training into the training algorithm to reduce the high sampling complexities. Numerical simulations demonstrate that QDQN-DPER outperforms the baseline distributed quantum Q learning with the same model architecture. The proposed framework holds potential for more complex tasks while maintaining training efficiency.

翻译：本文引入了 QDQN-DPER 框架，以增强量子强化学习（QRL）在解决序列决策任务时的效率。该框架将优先级经验回放和异步训练并入训练算法中，以减少高采样复杂度。数值模拟表明，QDQN-DPER 在与相同模型架构的分布式量子 Q 学习进行比较时表现更优。所提出的框架在保持培训效率的同时，在更复杂的任务方面具有潜力。