Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Bargiacchi, Eugenio"'
Publikováno v:
Reinforcement Learning Journal, vol. 1, no. 1, 2024, pp. TBD
In key real-world problems, full state information is sometimes available but only at a high cost, like activating precise yet energy-intensive sensors or consulting humans, thereby compelling the agent to operate under partial observability. For thi
Externí odkaz:
http://arxiv.org/abs/2407.18812
In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process. The set of optimal policies can grow exponentially with the number of objectives, and recovering all solutions requires an exhau
Externí odkaz:
http://arxiv.org/abs/2204.05036
Autor:
Hayes, Conor F., Rădulescu, Roxana, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa M., Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai A., Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik M.
Publikováno v:
Auton Agent Multi-Agent Syst 36, 26 (2022)
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a singl
Externí odkaz:
http://arxiv.org/abs/2103.09568
Autor:
Verstraeten, Timothy, Daems, Pieter-Jan, Bargiacchi, Eugenio, Roijers, Diederik M., Libin, Pieter J. K., Helsen, Jan
Wind farms are a crucial driver toward the generation of ecological and renewable energy. Due to their rapid increase in capacity, contemporary wind farms need to adhere to strict constraints on power output to ensure stability of the electricity gri
Externí odkaz:
http://arxiv.org/abs/2101.07844
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allows for sample-efficient learning on large problems by exploiting a fac
Externí odkaz:
http://arxiv.org/abs/2001.07527
Autor:
Verstraeten, Timothy, Bargiacchi, Eugenio, Libin, Pieter JK, Helsen, Jan, Roijers, Diederik M, Nowé, Ann
Publikováno v:
Sci Rep 10, 6728 (2020)
Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this regard is that agents in the real world often only directly affect a li
Externí odkaz:
http://arxiv.org/abs/1911.10120
Autor:
Hayes, Conor F., Radulescu, Roxana, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa, Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai A., Mannion, Patrick, Nowe, Ann, De Oliveira Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik M.
Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple -- often conflicting -- objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a si
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3848::52f887c4046d4b9f178b0a4484ac1013
https://hdl.handle.net/20.500.14017/6f906192-6285-47bd-8944-28f171bf01ce
https://hdl.handle.net/20.500.14017/6f906192-6285-47bd-8944-28f171bf01ce
Autor:
Verstraeten, Timothy1,2 tiverstr@vub.be, Bargiacchi, Eugenio1, Libin, Pieter J. K.1, Helsen, Jan2, Roijers, Diederik M.1,3, Nowé, Ann1
Publikováno v:
Scientific Reports. 4/21/2020, Vol. 10 Issue 1, p1-13. 13p.
Akademický článek
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Autor:
Bargiacchi, Eugenio, Avalos, Raphaël, Verstraeten, Timothy, Libin, Pieter, Nowe, Ann, Roijers, Diederik M.
In this paper, we provide PAC bounds for best-arm identification in multi-agent multi-armed bandits (MAMABs), via an algorithm we call multi-agent RMax (MARMax). In a MAMAB, the reward structure is expressed as a coordination graph, i.e., the total t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3848::dcd915ccf154b92ebea6601f3f5cad38
https://biblio.vub.ac.be/vubir/multiagent-rmax-for-multiagent-multiarmed-bandits(baf3359d-9492-4f3b-a75b-24bfdd697cef).html
https://biblio.vub.ac.be/vubir/multiagent-rmax-for-multiagent-multiarmed-bandits(baf3359d-9492-4f3b-a75b-24bfdd697cef).html