Model-based Offline Quantum Reinforcement Learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917913837961216 |
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| author | Eisenmann, Simon Hein, Daniel Udluft, Steffen Runkler, Thomas A. |
| author_facet | Eisenmann, Simon Hein, Daniel Udluft, Steffen Runkler, Thomas A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10017 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model-based Offline Quantum Reinforcement Learning Eisenmann, Simon Hein, Daniel Udluft, Steffen Runkler, Thomas A. Quantum Physics Artificial Intelligence Machine 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. |
| title | Model-based Offline Quantum Reinforcement Learning |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.10017 |