Distributed interference cancellation in multi-agent scenarios
This paper considers the problem of detecting impaired and noisy nodes over network. In a distributed algorithm, lots of processing units are incorporating and communicating with each other to reach a global goal. Due to each one's state in the shared environment, they can help the other nodes...
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| creator | Shamsi, Mahdi Haghighi, Alireza Moslemi Marvasti, Farokh |
| description | This paper considers the problem of detecting impaired and noisy nodes over
network. In a distributed algorithm, lots of processing units are incorporating
and communicating with each other to reach a global goal. Due to each one's
state in the shared environment, they can help the other nodes or mislead them
(due to noise or a deliberate attempt). Previous works mainly focused on proper
locating agents and weight assignment based on initial environment state to
minimize malfunctioning of noisy nodes. We propose an algorithm to be able to
adapt sharing weights according to behavior of the agents. Applying the
introduced algorithm to a multi-agent RL scenario and the well-known diffusion
LMS demonstrates its capability and generality. |
| doi_str_mv | 10.48550/arxiv.1910.10109 |
| format | Article |
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network. In a distributed algorithm, lots of processing units are incorporating
and communicating with each other to reach a global goal. Due to each one's
state in the shared environment, they can help the other nodes or mislead them
(due to noise or a deliberate attempt). Previous works mainly focused on proper
locating agents and weight assignment based on initial environment state to
minimize malfunctioning of noisy nodes. We propose an algorithm to be able to
adapt sharing weights according to behavior of the agents. Applying the
introduced algorithm to a multi-agent RL scenario and the well-known diffusion
LMS demonstrates its capability and generality.</description><identifier>DOI: 10.48550/arxiv.1910.10109</identifier><language>eng</language><subject>Computer Science - Learning ; Computer Science - Multiagent Systems ; Computer Science - Robotics ; Computer Science - Systems and Control</subject><creationdate>2019-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1910.10109$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1910.10109$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Shamsi, Mahdi</creatorcontrib><creatorcontrib>Haghighi, Alireza Moslemi</creatorcontrib><creatorcontrib>Marvasti, Farokh</creatorcontrib><title>Distributed interference cancellation in multi-agent scenarios</title><description>This paper considers the problem of detecting impaired and noisy nodes over
network. In a distributed algorithm, lots of processing units are incorporating
and communicating with each other to reach a global goal. Due to each one's
state in the shared environment, they can help the other nodes or mislead them
(due to noise or a deliberate attempt). Previous works mainly focused on proper
locating agents and weight assignment based on initial environment state to
minimize malfunctioning of noisy nodes. We propose an algorithm to be able to
adapt sharing weights according to behavior of the agents. Applying the
introduced algorithm to a multi-agent RL scenario and the well-known diffusion
LMS demonstrates its capability and generality.</description><subject>Computer Science - Learning</subject><subject>Computer Science - Multiagent Systems</subject><subject>Computer Science - Robotics</subject><subject>Computer Science - Systems and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj82KwjAUhbOZheg8gKvpC1RvTNMmG0F0_IGCG_flNr0ZAjUOaZSZtzf-cOAcOAcOfIxNOcwKJSXMMfy524zrVHDgoEdsuXFDDK69Ruoy5yMFS4G8ocxg8r7H6C4-Ldn52keX4w_5mA2GPAZ3GSbsw2I_0Oc7x-y0_T6t93l93B3WqzrHstLJuBKdEUqXoKBDRCgEtUprY5AnSc5lBboUQhFAYSVVpbULENJCu-jEmH29bp8AzW9wZwz_zQOkeYKIO94lQ1M</recordid><startdate>20191022</startdate><enddate>20191022</enddate><creator>Shamsi, Mahdi</creator><creator>Haghighi, Alireza Moslemi</creator><creator>Marvasti, Farokh</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20191022</creationdate><title>Distributed interference cancellation in multi-agent scenarios</title><author>Shamsi, Mahdi ; Haghighi, Alireza Moslemi ; Marvasti, Farokh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-a6183dc3896080daaa043eb899cca1a1a51157096338e004f5e76ff2035f0b2d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Learning</topic><topic>Computer Science - Multiagent Systems</topic><topic>Computer Science - Robotics</topic><topic>Computer Science - Systems and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Shamsi, Mahdi</creatorcontrib><creatorcontrib>Haghighi, Alireza Moslemi</creatorcontrib><creatorcontrib>Marvasti, Farokh</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shamsi, Mahdi</au><au>Haghighi, Alireza Moslemi</au><au>Marvasti, Farokh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distributed interference cancellation in multi-agent scenarios</atitle><date>2019-10-22</date><risdate>2019</risdate><abstract>This paper considers the problem of detecting impaired and noisy nodes over
network. In a distributed algorithm, lots of processing units are incorporating
and communicating with each other to reach a global goal. Due to each one's
state in the shared environment, they can help the other nodes or mislead them
(due to noise or a deliberate attempt). Previous works mainly focused on proper
locating agents and weight assignment based on initial environment state to
minimize malfunctioning of noisy nodes. We propose an algorithm to be able to
adapt sharing weights according to behavior of the agents. Applying the
introduced algorithm to a multi-agent RL scenario and the well-known diffusion
LMS demonstrates its capability and generality.</abstract><doi>10.48550/arxiv.1910.10109</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning Computer Science - Multiagent Systems Computer Science - Robotics Computer Science - Systems and Control |
| title | Distributed interference cancellation in multi-agent scenarios |
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