**arXiv ID:** 2304.09648 **Authors:** Samuel Yen-Chi Chen **Published:** 2023-04-19T13:40:44Z **Abstract:** 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 ...
Scanned 9/11/2026
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# Quantum deep Q learning with distributed prioritized experience replay
**arXiv ID:** 2304.09648
**Authors:** Samuel Yen-Chi Chen
**Published:** 2023-04-19T13:40:44Z
**Abstract:**
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.
## Skill Description
This skill is generated from the arXiv paper: Quantum deep Q learning with distributed prioritized experience replay (2304.09648).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:2304.09648](http://arxiv.org/abs/2304.09648v1)
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