- 學位論文
深度強化學習在多人連線戰鬥遊戲中隊伍選角推薦的運用
Deep Reinforcement Learning for Progressive Team Draft Recommendations in MOBA Games
摘要
MOBA是一種把玩家分成兩隊以互相進行對抗的電競遊戲種類。遊戲開始前，兩隊的隊員必須輪流從眾多的角色中選擇一個來操控。選角對MOBA來說是一個非常重要的環節，因為好的角色組合可以通過加成相互提升能力，而可以克制對手角色的角色則能讓對手能力大打折扣。在這篇論文裡，我們提出一個漸進式的角色推薦系統，可以在選角環節中的任何一個階段，針對目前隊友和對手已經選擇的角色，進行角色選擇推薦。業餘玩家和職業玩家都可以利用這套角色推薦系統在賽前做預測和制定戰略。遊戲開發者也可以利用這套系統對遊戲進行調整。這篇論文裡所有實驗都將會被進行在DOTA2，一個很流行的MOBA上。
並列摘要
Multiplayer Online Battle Arena (MOBA) is a genre of games in which two teams of players compete against each other. Both teams taking turns picking one draft at a time from a pool of characters before a match begins. The drafting phase can be a crucial phase, as a good combination of team characters and counter-combinations against the rival team, may highly improve the winning chances of a team. In this paper, we propose a progressive team draft recommendation system, that recommends one character to draft for a team, in each round of the drafting phase, given current enemy drafts and ally drafts. The recommendation system can be used by teams, both casual and professional, to make predictions and develop strategies before a match, or by game developers, to balance and fine-tune the game. All the experiments and results in this paper will be based on a popular MOBA called DOTA2.
參考文獻
延伸閱讀
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