Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Nguyen, Phuong D. H."'
Autor:
Eppe, Manfred, Gumbsch, Christian, Kerzel, Matthias, Nguyen, Phuong D. H., Butz, Martin V., Wermter, Stefan
Publikováno v:
Nature Machine Intelligence, 4(1) (2022)
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approa
Externí odkaz:
http://arxiv.org/abs/2208.08731
Autor:
Eppe, Manfred, Gumbsch, Christian, Kerzel, Matthias, Nguyen, Phuong D. H., Butz, Martin V., Wermter, Stefan
Publikováno v:
Nature Machine Intelligence, 4(1) (2022)
Cognitive Psychology and related disciplines have identified several critical mechanisms that enable intelligent biological agents to learn to solve complex problems. There exists pressing evidence that the cognitive mechanisms that enable problem-so
Externí odkaz:
http://arxiv.org/abs/2012.10147
Autor:
Nguyen, Phuong D. H., Georgie, Yasmin Kim, Kayhan, Ezgi, Eppe, Manfred, Hafner, Verena Vanessa, Wermter, Stefan
Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than
Externí odkaz:
http://arxiv.org/abs/2011.12860
Cognitive science suggests that the self-representation is critical for learning and problem-solving. However, there is a lack of computational methods that relate this claim to cognitively plausible robots and reinforcement learning. In this paper,
Externí odkaz:
http://arxiv.org/abs/2011.06985
Reinforcement learning is a promising method to accomplish robotic control tasks. The task of playing musical instruments is, however, largely unexplored because it involves the challenge of achieving sequential goals - melodies - that have a tempora
Externí odkaz:
http://arxiv.org/abs/2011.05715
Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity. However, there is a lack
Externí odkaz:
http://arxiv.org/abs/2005.03420
Reinforcement learning is an appropriate and successful method to robustly perform low-level robot control under noisy conditions. Symbolic action planning is useful to resolve causal dependencies and to break a causally complex problem down into a s
Externí odkaz:
http://arxiv.org/abs/1905.09683
Akademický článek
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Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity. However, there is a lack
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f6bec5813bd1e6c1b33ff1c77fc00f0
http://arxiv.org/abs/2005.03420
http://arxiv.org/abs/2005.03420
Publikováno v:
Frontiers in Psychology; 8/16/2021, Vol. 12, p1-16, 16p