Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866907817112240128 |
|---|---|
| author | Schenk, Michael Combarro, Elías F. Grossi, Michele Kain, Verena Li, Kevin Shing Bruce Popa, Mircea-Marian Vallecorsa, Sofia |
| author_facet | Schenk, Michael Combarro, Elías F. Grossi, Michele Kain, Verena Li, Kevin Shing Bruce Popa, Mircea-Marian Vallecorsa, Sofia |
| contents | Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the learning efficiency compared to classical Q-learning with the restriction, however, to discrete state-action space environments. In this paper, the FERL approach is extended to multi-dimensional continuous state-action space environments to open the doors for a broader range of real-world applications. First, free energy-based Q-learning is studied for discrete action spaces, but continuous state spaces and the impact of experience replay on sample efficiency is assessed. In a second step, a hybrid actor-critic scheme for continuous state-action spaces is developed based on the Deep Deterministic Policy Gradient algorithm combining a classical actor network with a QBM-based critic. The results obtained with quantum annealing, both simulated and with D-Wave quantum annealing hardware, are discussed, and the performance is compared to classical reinforcement learning methods. The environments used throughout represent existing particle accelerator beam lines at the European Organisation for Nuclear Research (CERN). Among others, the hybrid actor-critic agent is evaluated on the actual electron beam line of the Advanced Plasma Wakefield Experiment (AWAKE). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_11044 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines Schenk, Michael Combarro, Elías F. Grossi, Michele Kain, Verena Li, Kevin Shing Bruce Popa, Mircea-Marian Vallecorsa, Sofia Quantum Physics Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the learning efficiency compared to classical Q-learning with the restriction, however, to discrete state-action space environments. In this paper, the FERL approach is extended to multi-dimensional continuous state-action space environments to open the doors for a broader range of real-world applications. First, free energy-based Q-learning is studied for discrete action spaces, but continuous state spaces and the impact of experience replay on sample efficiency is assessed. In a second step, a hybrid actor-critic scheme for continuous state-action spaces is developed based on the Deep Deterministic Policy Gradient algorithm combining a classical actor network with a QBM-based critic. The results obtained with quantum annealing, both simulated and with D-Wave quantum annealing hardware, are discussed, and the performance is compared to classical reinforcement learning methods. The environments used throughout represent existing particle accelerator beam lines at the European Organisation for Nuclear Research (CERN). Among others, the hybrid actor-critic agent is evaluated on the actual electron beam line of the Advanced Plasma Wakefield Experiment (AWAKE). |
| title | Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2209.11044 |