Quantum reinforcement learning (QRL) has emerged as a framework to solve
sequential decision-making tasks, showcasing empirical quantum advantages. A
notable development is through quantum recurrent neural networks (QRNNs) for
memory-intensive tasks such as partially observable environments. However, QRL
models incorporating QRNN encounter challenges such as inefficient training of
QRL with QRNN, given that the computation of gradients in QRNN is both
computationally expensive and time-consuming. This work presents a novel
approach to address this challenge by constructing QRL agents utilizing
QRNN-based reservoirs, specifically employing quantum long short-term memory
(QLSTM). QLSTM parameters are randomly initialized and fixed without training.
The model is trained using the asynchronous advantage actor-aritic (A3C)
algorithm. Through numerical simulations, we validate the efficacy of our
QLSTM-Reservoir RL framework. Its performance is assessed on standard
benchmarks, demonstrating comparable results to a fully trained QLSTM RL model
with identical architecture and training settings.

通过构建基于 QRNN 的储层，并利用 QLSTM 构建量子强化学习代理，本文提出了一种新方法以应对 QRNN 在 QRL 中训练低效的挑战，并使用异步优势演员 - 评论家算法进行训练。通过数值模拟验证了 QLSTM-Reservoir RL 框架的有效性，并通过与完全训练的 QLSTM RL 模型在相同架构和训练设置下的性能进行评估。