Efficient quantum recurrent reinforcement learning via quantum reservoir
  computing
Samuel Yen-Chi Chen
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.