@prefix ocrer: <http://purl.org/net/OCRe/research.owl#> .
@prefix owl:   <http://www.w3.org/2002/07/owl#> .
@prefix scires: <http://vivoweb.org/ontology/scientific-research#> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix skos:  <http://www.w3.org/2004/02/skos/core#> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .
@prefix ocresd: <http://purl.org/net/OCRe/study_design.owl#> .
@prefix swo:   <http://www.ebi.ac.uk/efo/swo/> .
@prefix cito:  <http://purl.org/spar/cito/> .
@prefix geo:   <http://aims.fao.org/aos/geopolitical.owl#> .
@prefix ocresst: <http://purl.org/net/OCRe/statistics.owl#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix vivo:  <http://vivoweb.org/ontology/core#> .
@prefix event: <http://purl.org/NET/c4dm/event.owl#> .
@prefix vann:  <http://purl.org/vocab/vann/> .
@prefix foaf:  <http://xmlns.com/foaf/0.1/> .
@prefix c4o:   <http://purl.org/spar/c4o/> .
@prefix fabio: <http://purl.org/spar/fabio/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix thkoeln: <http://cris.nrw/hisinone#> .
@prefix vitro: <http://vitro.mannlib.cornell.edu/ns/vitro/0.7#> .
@prefix vitro-public: <http://vitro.mannlib.cornell.edu/ns/vitro/public#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ocresp: <http://purl.org/net/OCRe/study_protocol.owl#> .
@prefix bibo:  <http://purl.org/ontology/bibo/> .
@prefix obo:   <http://purl.obolibrary.org/obo/> .
@prefix ro:    <http://purl.obolibrary.org/obo/ro.owl#> .

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<https://fis.th-koeln.de/vivo/individual/FF_001>
        <http://cris.nrw/hisinone/istForschungsfeldvon>
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        rdfs:label                "AlphaZero-Inspired Game Learning" , "RDF description of AlphaZero-Inspired Game Learning - https://fis.th-koeln.de/vivo/individual/publ_14036" ;
        thkoeln:dokumententyp     "Wissenschaftlicher Artikel" ;
        thkoeln:peerReviewed      "true" ;
        thkoeln:sprache           "englisch" ;
        thkoeln:status            "validiert" ;
        thkoeln:subtitle          "Faster Training by Using MCTS Only at Test Time" ;
        <http://cris.nrw/hisinone/hatForschungsfeld>
                <https://fis.th-koeln.de/vivo/individual/FF_001> ;
        <http://cris.nrw/hisinone/hatOrganisationseinheit>
                <https://fis.th-koeln.de/vivo/individual/10000151> ;
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                <https://fis.th-koeln.de/vivo/individual/cite_14036-2> , <https://fis.th-koeln.de/vivo/individual/cite_14036-1> , <https://fis.th-koeln.de/vivo/individual/cite_14036-0> ;
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        bibo:abstract             "Recently, the seminal algorithms AlphaGo and AlphaZero  have started a new era in game learning and deep  reinforcement learning. While the achievements of AlphaGo and  AlphaZero – playing Go and other complex games at super  human level – are truly impressive, these architectures have  the drawback that they require high computational resources.  Many researchers are looking for methods that are similar to  AlphaZero, but have lower computational demands and are thus  more easily reproducible.  In this paper, we pick an important element of AlphaZero –  the Monte Carlo Tree Search (MCTS) planning stage – and  combine it with temporal difference (TD) learning agents. We  wrap MCTS for the first time around TD n-tuple networks and  we use this wrapping only at test time to create versatile agents  that keep at the same time the computational demands low. We  apply this new architecture to several complex games (Othello,  ConnectFour, Rubik’s Cube) and show the advantages achieved  with this AlphaZero-inspired MCTS wrapper. In particular, we  present results that this agent is the first one trained on standard  hardware (no GPU or TPU) to beat the very strong Othello  program Edax up to and including level 7 (where most other  learning-from-scratch algorithms could only defeat Edax up to  level 2)." ;
        bibo:doi                  "10.1109/TG.2022.3206733" ;
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        vivo:dateTimeValue        <http://cris.nrw/date2022> ;
        vivo:freetextKeyword      "Task Analysis" , "Games" , "Prediction Algorithms" , "Planning" , "Silver" , "Training" , "Wrapping" ;
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