Reinforcement Learning and Physics
Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environmen...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/128544 |
| Acceso en línea: | https://hdl.handle.net/11441/128544 https://doi.org/10.3390/app11188589 |
| Access Level: | acceso abierto |
| Palabra clave: | reinforcement learning physics artificial intelligence machine learning quantum technologies quantum machine learning quantum reinforcement learning |
| Sumario: | Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of reinforcement learning and physics. We include standard reinforcement learning techniques in the computer science community for enhancing physics research, as well as the more recent and emerging area of quantum reinforcement learning, inside quantum machine learning, for improving reinforcement learning computations. |
|---|