Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Kagan Tumer"'
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference Companion.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6046bda09db72967e2c04a9a5bc74a5d
https://doi.org/10.1145/3520304.3529035
https://doi.org/10.1145/3520304.3529035
Autor:
Gaurav Dixit, Kagan Tumer
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference Companion.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ee193ef263ea20ca26062ed778429cc
https://doi.org/10.1145/3520304.3529062
https://doi.org/10.1145/3520304.3529062
Autor:
Golden Rockefeller, Kagan Tumer
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference Companion.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c49b0ec824a2a7e4e1bd05ad869d41c
https://doi.org/10.1145/3520304.3528999
https://doi.org/10.1145/3520304.3528999
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe9c5b3ccfb16052c61a0806a0e0f211
https://doi.org/10.1145/3512290.3528860
https://doi.org/10.1145/3512290.3528860
Autor:
Joshua Cook, Kagan Tumer
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b819def1f32ceda3a3b952e1b96537b0
https://doi.org/10.1145/3512290.3528829
https://doi.org/10.1145/3512290.3528829
Publikováno v:
2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::43cbf20112db21d1551e64c80cbc73b3
https://doi.org/10.1109/mrs50823.2021.9620689
https://doi.org/10.1109/mrs50823.2021.9620689
Autor:
Kagan Tumer, Joshua Cook
Publikováno v:
GECCO Companion
Cooperative Co-evolutionary Algorithms effectively train policies in multiagent systems with a single, statically defined team. However, many real-world problems, such as search and rescue, require agents to operate in multiple teams. When the struct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21307d92ee9960921083a3efbabbb912
https://doi.org/10.1145/3449726.3459560
https://doi.org/10.1145/3449726.3459560
Publikováno v:
GECCO Companion
Many long term robot exploration domains have sparse fitness functions that make it hard for agents to learn and adapt. This work introduces Adaptive Multi-Fitness Learning (A-MFL), which augments the structure of Multi-Fitness Learning (MFL) [7] by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8149fa09cae4eb3098826f903e614d8d
https://doi.org/10.1145/3449726.3459563
https://doi.org/10.1145/3449726.3459563
Publikováno v:
GECCO Companion
In many real-world multiagent systems, agents must learn diverse tasks and coordinate with other agents. This paper introduces a method to allow heterogeneous agents to specialize and only learn complementary divergent behaviors needed for coordinati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee4dd93bb98232170741f7323b61d371
https://doi.org/10.1145/3449726.3459564
https://doi.org/10.1145/3449726.3459564
Publikováno v:
GECCO
Evolving effective coordination strategies in tightly coupled multi-agent settings with sparse team fitness evaluations is challenging. It relies on multiple agents simultaneously stumbling upon the goal state to generate a learnable feedback signal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d431a8c7a34c7aeaf06871eab7c2f82
https://doi.org/10.1145/3449639.3459387
https://doi.org/10.1145/3449639.3459387