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Playing Atari with few neurons: Improving the efficacy of reinforcement learning by decoupling feature extraction and decision making
We propose a new method for learning compact state representations and policies separately but simultaneously for policy approximation in vision-based applications such as Atari games. Approaches based on deep reinforcement learning typically map pixels directly to actions to enable end-to-end train...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550197/ https://www.ncbi.nlm.nih.gov/pubmed/34720684 http://dx.doi.org/10.1007/s10458-021-09497-8 |