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Deep reinforcement learning in strategic board game environments

Xenou Konstantia, Chalkiadakis Georgios, Afantenos, Stergos, 1978-...., enseignant-chercheur en informatique

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/62B998F8-AF43-4440-A3D9-43A04588D7C4-
Αναγνωριστικόhttps://doi.org/10.1007/978-3-030-14174-5_16-
Αναγνωριστικόhttps://link.springer.com/chapter/10.1007/978-3-030-14174-5_16-
Γλώσσαen-
Μέγεθος16 pagesen
ΤίτλοςDeep reinforcement learning in strategic board game environmentsen
ΔημιουργόςXenou Konstantiaen
ΔημιουργόςΞενου Κωνσταντιαel
ΔημιουργόςChalkiadakis Georgiosen
ΔημιουργόςΧαλκιαδακης Γεωργιοςel
ΔημιουργόςAfantenos, Stergos, 1978-...., enseignant-chercheur en informatiqueen
ΕκδότηςSpringer Natureen
ΠερίληψηIn this paper we propose a novel Deep Reinforcement Learning (DRL) algorithm that uses the concept of “action-dependent state features”, and exploits it to approximate the Q-values locally, employing a deep neural network with parallel Long Short Term Memory (LSTM) components, each one responsible for computing an action-related Q-value. As such, all computations occur simultaneously, and there is no need to employ “target” networks and experience replay, which are techniques regularly used in the DRL literature. Moreover, our algorithm does not require previous training experiences, but trains itself online during game play. We tested our approach in the Settlers Of Catan multi-player strategic board game. Our results confirm the effectiveness of our approach, since it outperforms several competitors, including the state-of-the-art jSettler heuristic algorithm devised for this particular domain.en
ΤύποςΚεφάλαιο σε Βιβλίοel
ΤύποςBook Chapteren
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2020-10-29-
Ημερομηνία Δημοσίευσης2019-
Θεματική ΚατηγορίαDeep Reinforcement Learningen
Θεματική ΚατηγορίαStrategic board gamesen
Θεματική ΚατηγορίαNeural networken
Βιβλιογραφική ΑναφοράK. Xenou, G. Chalkiadakis and S. Afantenos, "Deep reinforcement learning in strategic board game environments," in Multi-Agent Systems. EUMAS 2018, vol. 11450, Lecture Notes in Computer Science, M. Slavkovik, Ed., Cham, Switzerland: Springer Nature, 2019, pp. 233-248. doi: 10.1007/978-3-030-14174-5_16en
Τίτλος ΒιβλίουMulti-Agent Systems. EUMAS 2018en
Σειρά ΒιβλίουLecture Notes in Computer Science en

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