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：本文提出QDQN-DPER框架，旨在提升量子强化学习在解决序列决策任务中的效率。该框架将优先经验回放与异步训练融入训练算法，以降低高采样复杂度。数值仿真表明，在相同模型架构下，QDQN-DPER优于基线分布式量子Q学习。所提出的框架在保持训练效率的同时，具备应用于更复杂任务的潜力。