论文标题
量子深度重复增强学习
Quantum deep recurrent reinforcement learning
论文作者
论文摘要
量子计算（QC）和机器学习（ML）的最新进展已引起人们对量子机学习（QML）的开发的极大关注。增强学习（RL）是可用于解决复杂的顺序决策问题的ML范式之一。经典RL已被证明能够解决各种具有挑战性的任务。但是，量子世界中的RL算法仍处于起步阶段。尚未解决的挑战之一是如何在可观察到的环境中训练量子RL。在本文中，我们通过构建具有量子复发网络（QRNN）的QRL代理来应对这一挑战。具体来说，我们选择量子长的短期内存（QLSTM）作为QRL代理的核心，并用深层$ q $ - 学习训练整个模型。我们通过数值模拟证明了结果，QLSTM-DRQN可以求解标准基准测试，例如与具有相似体系结构和模型参数数量的经典DRQN相比，具有更稳定和更高的平均得分的Cart-Pole。
Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML). Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex sequential decision making problems. Classical RL has been shown to be capable to solve various challenging tasks. However, RL algorithms in the quantum world are still in their infancy. One of the challenges yet to solve is how to train quantum RL in the partially observable environments. In this paper, we approach this challenge through building QRL agents with quantum recurrent neural networks (QRNN). Specifically, we choose the quantum long short-term memory (QLSTM) to be the core of the QRL agent and train the whole model with deep $Q$-learning. We demonstrate the results via numerical simulations that the QLSTM-DRQN can solve standard benchmark such as Cart-Pole with more stable and higher average scores than classical DRQN with similar architecture and number of model parameters.
