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Selective particle attention: Rapidly and flexibly selecting features for deep reinforcement learning
Deep Reinforcement Learning (RL) is often criticised for being data inefficient and inflexible to changes in task structure. Part of the reason for these issues is that Deep RL typically learns end-to-end using backpropagation, which results in task-specific representations. One approach for circumv...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Pergamon Press
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9037388/ https://www.ncbi.nlm.nih.gov/pubmed/35358888 http://dx.doi.org/10.1016/j.neunet.2022.03.015 |