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pro vyhledávání: '"Sartor, Gabriele"'
Recently, AI systems have made remarkable progress in various tasks. Deep Reinforcement Learning(DRL) is an effective tool for agents to learn policies in low-level state spaces to solve highly complex tasks. Researchers have introduced Intrinsic Mot
Externí odkaz:
http://arxiv.org/abs/2409.11756
Deforestation is gaining an increasingly importance due to its strong influence on the sorrounding environment, especially in developing countries where population has a disadvantaged economic condition and agriculture is the main source of income. I
Externí odkaz:
http://arxiv.org/abs/2409.11186
Autor:
Sartor, Gabriele, Zollo, Davide, Mayer, Marta Cialdea, Oddi, Angelo, Rasconi, Riccardo, Santucci, Vieri Giuliano
In this work we present an empirical study where we demonstrate the possibility of developing an artificial agent that is capable to autonomously explore an experimental scenario. During the exploration, the agent is able to discover and learn intere
Externí odkaz:
http://arxiv.org/abs/2206.01815
Autor:
Oddi, Angelo, Rasconi, Riccardo, Cartoni, Emilio, Sartor, Gabriele, Baldassarre, Gianluca, Santucci, Vieri Giuliano
In symbolic planning systems, the knowledge on the domain is commonly provided by an expert. Recently, an automatic abstraction procedure has been proposed in the literature to create a Planning Domain Definition Language (PDDL) representation, which
Externí odkaz:
http://arxiv.org/abs/1907.08313
Akademický článek
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Publikováno v:
Sensors (14248220); Sep2023, Vol. 23 Issue 17, p7632, 25p
Publikováno v:
Social Sciences (2076-0760); Jul2023, Vol. 12 Issue 7, p398, 29p
Publikováno v:
Scopus-Elsevier
In this work we present an empirical study where we demonstrate the possibility of developing an arti- ficial agent that is capable to autonomously explore an experimental scenario. During the exploration, the agent is able to discover and learn inte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::70e1ebbaad4c0f6c5a738372c1d83998
https://hdl.handle.net/2318/1885231
https://hdl.handle.net/2318/1885231