Training agents to play in contemporary multiplayer actions game is a challenging task, especially when agents are expected to cooperate in a hostile environment while performing several different actions at the same time. Nonetheless, this topic is assuming a growing importance due to the rampaging diffusion of this game genre and its related e-sports. Agents playing in a multiplayer survival first person shooter game should mimic a human player, hence they should learn how to: survive in unexplored environment, improve their combat skills, deal with unexpected events, coordinate with allies and reach a good ranking among the players community. Our aim has been to design, develop and test a preliminary solution that exploits Proximal Policy Optimization algorithms to train agents without the need of a human expert, with the final goal of creating teams composed only by artificial players.
Deep reinforcement learning to train agents in a multiplayer first person shooter: Some preliminary results / D. Piergigli, L.A. Ripamonti, D. Maggiorini, D. Gadia (IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND GAMES). - In: 2019 IEEE Conference on Games (CoG)[s.l] : IEEE, 2019. - ISBN 9781728118840. - pp. 1-8 (( convegno IEEE Conference on Games, CoG tenutosi a London nel 2019.
|Titolo:||Deep reinforcement learning to train agents in a multiplayer first person shooter: Some preliminary results|
RIPAMONTI, LAURA ANNA (Corresponding)
|Parole Chiave:||machine learning; neural network; deep reinforcement learning; e-sports; video games; shooter games; artificial intelligence; first person shooter game; artificial player|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2019|
|Enti collegati al convegno:||Board Game Cafe Draughts|
IEEE Computational Intelligence Society
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/CIG.2019.8848061|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|