This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the policy to be optimized are each implemented as variational quantum circuits. The model is trained by gradient descent to fit a pre-recorded data set. The policy is optimized with a gradient-free optimization scheme using the return estimate given by the model as the fitness function. This model-based approach allows, in principle, full realization on a quantum computer during the optimization phase and gives hope that a quantum advantage can be achieved as soon as sufficiently powerful quantum computers are available.
@article{arxiv.2404.10017,
title = {Model-based Offline Quantum Reinforcement Learning},
author = {Simon Eisenmann and Daniel Hein and Steffen Udluft and Thomas A. Runkler},
journal= {arXiv preprint arXiv:2404.10017},
year = {2025}
}