Quantum deep recurrent reinforcement learning
This addresses a challenge in quantum machine learning for researchers, but it is incremental as it builds on existing quantum and classical methods.
The paper tackles training quantum reinforcement learning agents in partially observable environments by using quantum recurrent neural networks, specifically QLSTM with deep Q-learning, and demonstrates through simulations that their QLSTM-DRQN achieves more stable and higher average scores on the Cart-Pole benchmark compared to classical DRQN with similar parameters.
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.