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Quantum Reinforcement Learning (QRL) emerged as a branch of reinforcementlearning (RL) that uses quantum submodules in the architecture of thealgorithm. One branch of QRL focuses on the replacement of neural networks (NN)by variational quantum circuits (VQC) as function approximators. Initial workshave shown promising results on classical environments with discrete actionspaces, but many of the proposed architectural design choices of the VQC lack adetailed investigation. Hence, in this work we investigate the impact of VQCdesign choices such as angle embedding, encoding block architecture andpostprocessesing on the training capabilities of QRL agents. We show that VQCdesign greatly influences training performance and heuristically deriveenhancements for the analyzed components. Additionally, we show how to design aQRL agent in order to solve classical environments with continuous actionspaces and benchmark our agents against classical feed-forward NNs.

Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments