{"id":"https://openalex.org/W4389315180","doi":"https://doi.org/10.1109/cog57401.2023.10333223","title":"PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games","display_name":"PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games","publication_year":2023,"publication_date":"2023-08-21","ids":{"openalex":"https://openalex.org/W4389315180","doi":"https://doi.org/10.1109/cog57401.2023.10333223"},"language":"en","primary_location":{"id":"doi:10.1109/cog57401.2023.10333223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cog57401.2023.10333223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Conference on Games (CoG)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/94734/2/PyTAG%3a%20Challenges%20and%20Opportunities%20for%20Reinforcement%20Learning%20in%20Tabletop%20Games%2c%202023%20accepted.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089072718","display_name":"Martin Balla","orcid":"https://orcid.org/0009-0009-2558-2100"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Martin Balla","raw_affiliation_strings":["Queen Mary University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary University of London","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071877564","display_name":"George E.M. Long","orcid":"https://orcid.org/0009-0007-3509-2055"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"George E.M. Long","raw_affiliation_strings":["Queen Mary University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary University of London","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051707576","display_name":"Dominik Jeurissen","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dominik Jeurissen","raw_affiliation_strings":["Queen Mary University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary University of London","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042402616","display_name":"James Goodman","orcid":"https://orcid.org/0000-0002-8966-5644"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"James Goodman","raw_affiliation_strings":["Queen Mary University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary University of London","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044149802","display_name":"Raluca D. Gaina","orcid":"https://orcid.org/0000-0002-0283-8312"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Raluca D. Gaina","raw_affiliation_strings":["Queen Mary University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary University of London","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058274276","display_name":"Diego P\u00e9rez-Li\u00e9bana","orcid":"https://orcid.org/0000-0003-1958-0212"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Diego Perez-Liebana","raw_affiliation_strings":["Queen Mary University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary University of London","institution_ids":["https://openalex.org/I166337079"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I166337079"],"apc_list":null,"apc_paid":null,"fwci":0.4759,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70145245,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11197","display_name":"Digital Games and Media","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8381303548812866},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8287388682365417},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.7594144940376282},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.6000185608863831},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.5037049651145935},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4637508988380432},{"id":"https://openalex.org/keywords/video-game","display_name":"Video game","score":0.4219900071620941},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3552458882331848},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.3454674780368805},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11044329404830933}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8381303548812866},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8287388682365417},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.7594144940376282},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.6000185608863831},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.5037049651145935},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4637508988380432},{"id":"https://openalex.org/C3018412434","wikidata":"https://www.wikidata.org/wiki/Q7889","display_name":"Video game","level":2,"score":0.4219900071620941},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3552458882331848},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.3454674780368805},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11044329404830933},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cog57401.2023.10333223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cog57401.2023.10333223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Conference on Games (CoG)","raw_type":"proceedings-article"},{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/94734","is_oa":true,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/94734","pdf_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/94734/2/PyTAG%3a%20Challenges%20and%20Opportunities%20for%20Reinforcement%20Learning%20in%20Tabletop%20Games%2c%202023%20accepted.pdf","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/94734","is_oa":true,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/94734","pdf_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/94734/2/PyTAG%3a%20Challenges%20and%20Opportunities%20for%20Reinforcement%20Learning%20in%20Tabletop%20Games%2c%202023%20accepted.pdf","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G752382495","display_name":"EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI)","funder_award_id":"EP/S022325/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389315180.pdf","grobid_xml":"https://content.openalex.org/works/W4389315180.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2126316555","https://openalex.org/W2140629606","https://openalex.org/W2150468603","https://openalex.org/W2165779045","https://openalex.org/W2574978968","https://openalex.org/W2736601468","https://openalex.org/W2750737324","https://openalex.org/W2772709170","https://openalex.org/W2913781869","https://openalex.org/W2943930939","https://openalex.org/W2963369679","https://openalex.org/W2969287672","https://openalex.org/W2977093897","https://openalex.org/W2979533262","https://openalex.org/W2982438303","https://openalex.org/W3012544020","https://openalex.org/W3129426834","https://openalex.org/W3130564053","https://openalex.org/W3156543906","https://openalex.org/W3213774989","https://openalex.org/W4206115166","https://openalex.org/W4229503917","https://openalex.org/W4296512948","https://openalex.org/W4301654628","https://openalex.org/W4309663019","https://openalex.org/W4312790613","https://openalex.org/W4364360873","https://openalex.org/W6775683342","https://openalex.org/W6795531028","https://openalex.org/W6796417174","https://openalex.org/W6803914020"],"related_works":["https://openalex.org/W2341492732","https://openalex.org/W3187193180","https://openalex.org/W106542691","https://openalex.org/W1699080303","https://openalex.org/W4297799326","https://openalex.org/W3116064965","https://openalex.org/W4287027380","https://openalex.org/W3193760048","https://openalex.org/W4285822516","https://openalex.org/W2505261959"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"Game":[3],"AI":[4,75],"research":[5,117],"has":[6,21],"made":[7],"important":[8],"breakthroughs":[9],"using":[10],"Reinforcement":[11],"Learning":[12],"(RL).":[13],"Despite":[14],"this,":[15],"RL":[16,82,116],"for":[17,50,74,80],"modern":[18,67,109],"tabletop":[19,68,110],"games":[20,86,111],"gained":[22],"little":[23],"to":[24,37,115],"no":[25],"attention,":[26],"even":[27],"when":[28],"they":[29],"offer":[30],"a":[31,47,60,71,98],"range":[32],"of":[33,63,100],"unique":[34,106],"challenges":[35,107],"compared":[36],"video":[38],"games.":[39,101],"To":[40],"bridge":[41],"this":[42],"gap,":[43],"we":[44,103],"introduce":[45,88],"PyTAG,":[46],"Python":[48],"API":[49,73],"interacting":[51],"with":[52,70],"the":[53,105],"Tabletop":[54],"Games":[55],"framework":[56],"(TAG).":[57],"TAG":[58],"contains":[59],"growing":[61],"set":[62],"more":[64],"than":[65],"20":[66],"games,":[69],"common":[72],"agents.":[76],"We":[77],"present":[78],"techniques":[79],"training":[81,92],"agents":[83],"in":[84],"these":[85],"and":[87],"baseline":[89],"results":[90],"after":[91],"Proximal":[93],"Policy":[94],"Optimisation":[95],"algorithms":[96],"on":[97],"subset":[99],"Finally,":[102],"discuss":[104],"complex":[108],"provide,":[112],"now":[113],"open":[114],"through":[118],"PyTAG.":[119]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}