Quantum deep Q learning with distributed prioritized experience replay
This work addresses efficiency issues in quantum reinforcement learning for sequential decision tasks, but it appears incremental as it builds on existing quantum Q-learning methods.
The paper tackles the high sampling complexity in quantum reinforcement learning by introducing the QDQN-DPER framework, which incorporates prioritized experience replay and asynchronous training, and numerical simulations show it outperforms a baseline distributed quantum Q-learning method with the same architecture.
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.