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This paper introduces the QDQN-DPER framework to enhance the efficiency ofquantum reinforcement learning (QRL) in solving sequential decision tasks. Theframework incorporates prioritized experience replay and asynchronous traininginto the training algorithm to reduce the high sampling complexities. Numericalsimulations demonstrate that QDQN-DPER outperforms the baseline distributedquantum Q learning with the same model architecture. The proposed frameworkholds potential for more complex tasks while maintaining training efficiency.

Quantum deep Q learning with distributed prioritized experience replay