Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Avalos, Raphaël"'
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
We introduce the Laser Learning Environment (LLE), a collaborative multi-agent reinforcement learning environment in which coordination is central. In LLE, agents depend on each other to make progress (interdependence), must jointly take specific seq
Externí odkaz:
http://arxiv.org/abs/2404.03596
Communication plays a vital role in multi-agent systems, fostering collaboration and coordination. However, in real-world scenarios where communication is bandwidth-limited, existing multi-agent reinforcement learning (MARL) algorithms often provide
Externí odkaz:
http://arxiv.org/abs/2306.10134
Partially Observable Markov Decision Processes (POMDPs) are used to model environments where the full state cannot be perceived by an agent. As such the agent needs to reason taking into account the past observations and actions. However, simply reme
Externí odkaz:
http://arxiv.org/abs/2303.03284
Publikováno v:
Transactions on Machine Learning Research - October 2023
Many recent successful off-policy multi-agent reinforcement learning (MARL) algorithms for cooperative partially observable environments focus on finding factorized value functions, leading to convoluted network structures. Building on the structure
Externí odkaz:
http://arxiv.org/abs/2112.12458
Job scheduling is a central element of our society. While this problem has been actively researched by the field Operations Research (OR), yielding good algorithms, these solutions are usually not generalizable across different problem instances. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3848::7929911ee19303bcafec7f7f1e97d156
https://biblio.vub.ac.be/vubir/tackling-scheduling-problems-with-graph-structured-reinforcement-learning(78557de7-6694-4725-9e3f-33bf341e8d77).html
https://biblio.vub.ac.be/vubir/tackling-scheduling-problems-with-graph-structured-reinforcement-learning(78557de7-6694-4725-9e3f-33bf341e8d77).html
The competitive and cooperative forces of natural selection have driven the evolution of intelligence for many millions of years, eventually culminating in nature’s vast biodiversity and the complexity of our human minds. In this paper, we present
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3848::277dfbaf0e06e031e553a0054fb9c02b
https://biblio.vub.ac.be/vubir/autocurricula-and-emergent-sociality-from-a-gene-perspective(6ae84f6b-d6bc-4e21-9cef-6fb17c523f9d).html
https://biblio.vub.ac.be/vubir/autocurricula-and-emergent-sociality-from-a-gene-perspective(6ae84f6b-d6bc-4e21-9cef-6fb17c523f9d).html
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