Towards data-efficient deployment of reinforcement learning systems
<p>A fundamental concern in the deployment of artificial agents in real-life is their capacity to quickly adapt to their surroundings. Traditional reinforcement learning (RL) struggles with this requirement in two ways. Firstly, iterative exploration of unconstrained environment dynamics yield...
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| Formato: | Thesis |
| Idioma: | English |
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2021
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