Model-based Offline Quantum Reinforcement Learning
<|reference_start|>Model-based Offline Quantum Reinforcement Learning: 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.<|reference_end|>
@article{eisenmann2024model-based,
    title={Model-based Offline Quantum Reinforcement Learning},
    author={Simon Eisenmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler},
    journal={arXiv preprint arXiv:2404.10017},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10017},
    primaryClass={quant-ph cs.AI cs.LG}
}