Model-based Offline Quantum Reinforcement Learning

• URL: http://arxiv.org/abs/2404.10017v1
• Date: Sun, 14 Apr 2024 15:11:27 GMT
• Title: Model-based Offline Quantum Reinforcement Learning
• Authors: Simon Eisenmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler,
• Abstract summary: This paper presents the first algorithm for model-based offline quantum reinforcement learning. It gives hope that a quantum advantage can be achieved as soon as sufficiently powerful quantum computers are available.
• Score: 4.912318087940015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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