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pro vyhledávání: '"Conserva, Michelangelo"'
Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-based algorithms promise sample efficiency by building an environment mode
Externí odkaz:
http://arxiv.org/abs/2305.00477
Autor:
Conserva, Michelangelo, Rauber, Paulo
Meticulously analysing the empirical strengths and weaknesses of reinforcement learning methods in hard (challenging) environments is essential to inspire innovations and assess progress in the field. In tabular reinforcement learning, there is no we
Externí odkaz:
http://arxiv.org/abs/2210.13075
Publikováno v:
Transactions on Machine Learning Research (2022)
Many algorithms for ranked data become computationally intractable as the number of objects grows due to the complex geometric structure induced by rankings. An additional challenge is posed by partial rankings, i.e. rankings in which the preference
Externí odkaz:
http://arxiv.org/abs/2105.12356
Publikováno v:
Neural Computation. 2022 Oct 7;34(11):2232-72
An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences. Handcrafting an appropriate historical context is an attractive al
Externí odkaz:
http://arxiv.org/abs/2007.04750
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