Intelligent planning method for combat resource cross-domain allocation problem
The invention provides an intelligent planning method for a combat resource cross-domain allocation problem. The intelligent planning method comprises the following steps: designing a communication-based multi-agent reinforcement learning combat resource cross-domain allocation solving environment;...
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creator | LYU NAIBING ZHAO RUOFAN DONG QIAN WANG YI LIU QINGGUO WANG CAIHONG XU XINYUE XING GUYAN |
description | The invention provides an intelligent planning method for a combat resource cross-domain allocation problem. The intelligent planning method comprises the following steps: designing a communication-based multi-agent reinforcement learning combat resource cross-domain allocation solving environment; establishing a proximity strategy optimization network model for cross-domain deployment of multi-agent reinforcement learning combat resources based on communication; training a communication-based multi-agent reinforcement learning combat resource cross-domain deployment proximity policy optimization network model to obtain a trained proximity policy optimization network model; the trained proximity strategy optimization network model is used for testing, and a combat resource cross-domain scheduling problem is solved; and multi-agent combat resource cross-domain deployment deep reinforcement learning network optimization is carried out for application scene changes, and self-learning and online upgrading of the |
format | Patent |
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The intelligent planning method comprises the following steps: designing a communication-based multi-agent reinforcement learning combat resource cross-domain allocation solving environment; establishing a proximity strategy optimization network model for cross-domain deployment of multi-agent reinforcement learning combat resources based on communication; training a communication-based multi-agent reinforcement learning combat resource cross-domain deployment proximity policy optimization network model to obtain a trained proximity policy optimization network model; the trained proximity strategy optimization network model is used for testing, and a combat resource cross-domain scheduling problem is solved; and multi-agent combat resource cross-domain deployment deep reinforcement learning network optimization is carried out for application scene changes, and self-learning and online upgrading of the</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230530&DB=EPODOC&CC=CN&NR=116187787A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25543,76294</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230530&DB=EPODOC&CC=CN&NR=116187787A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LYU NAIBING</creatorcontrib><creatorcontrib>ZHAO RUOFAN</creatorcontrib><creatorcontrib>DONG QIAN</creatorcontrib><creatorcontrib>WANG YI</creatorcontrib><creatorcontrib>LIU QINGGUO</creatorcontrib><creatorcontrib>WANG CAIHONG</creatorcontrib><creatorcontrib>XU XINYUE</creatorcontrib><creatorcontrib>XING GUYAN</creatorcontrib><title>Intelligent planning method for combat resource cross-domain allocation problem</title><description>The invention provides an intelligent planning method for a combat resource cross-domain allocation problem. 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The intelligent planning method comprises the following steps: designing a communication-based multi-agent reinforcement learning combat resource cross-domain allocation solving environment; establishing a proximity strategy optimization network model for cross-domain deployment of multi-agent reinforcement learning combat resources based on communication; training a communication-based multi-agent reinforcement learning combat resource cross-domain deployment proximity policy optimization network model to obtain a trained proximity policy optimization network model; the trained proximity strategy optimization network model is used for testing, and a combat resource cross-domain scheduling problem is solved; and multi-agent combat resource cross-domain deployment deep reinforcement learning network optimization is carried out for application scene changes, and self-learning and online upgrading of the</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
title | Intelligent planning method for combat resource cross-domain allocation problem |
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