  The development of quantum machine learning (QML) has received a lot of
interest recently thanks to developments in both quantum computing (QC) and
machine learning (ML). One of the ML paradigms that can be utilized to address
challenging sequential decision-making issues is reinforcement learning (RL).
It has been demonstrated that classical RL can successfully complete many
difficult tasks. A leading method of building quantum RL agents relies on the
variational quantum circuits (VQC). However, training QRL algorithms with VQCs
requires significant amount of computational resources. This issue hurdles the
exploration of various QRL applications. In this paper, we approach this
challenge through asynchronous training QRL agents. Specifically, we choose the
asynchronous training of advantage actor-critic variational quantum policies.
We demonstrate the results via numerical simulations that within the tasks
considered, the asynchronous training of QRL agents can reach performance
comparable to or superior than classical agents with similar model sizes and
architectures.
