Action2Score: An Embedding Approach To Score Player Action
- URL: http://arxiv.org/abs/2207.10297v1
- Date: Thu, 21 Jul 2022 04:23:14 GMT
- Title: Action2Score: An Embedding Approach To Score Player Action
- Authors: Junho Jang, Ji Young Woo, Huy Kang Kim
- Abstract summary: In most Multiplayer Online Battle Arena (MOBA) games, a player's rank is determined by the match result (win or lose)
We propose a novel embedding model that converts a player's actions into quantitative scores based on the actions' respective contribution to the team's victory.
Our model is built using a sequence-based deep learning model with a novel loss function working on the team match.
- Score: 4.383011485317949
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multiplayer Online Battle Arena (MOBA) is one of the most successful game
genres. MOBA games such as League of Legends have competitive environments
where players race for their rank. In most MOBA games, a player's rank is
determined by the match result (win or lose). It seems natural because of the
nature of team play, but in some sense, it is unfair because the players who
put a lot of effort lose their rank just in case of loss and some players even
get free-ride on teammates' efforts in case of a win. To reduce the
side-effects of the team-based ranking system and evaluate a player's
performance impartially, we propose a novel embedding model that converts a
player's actions into quantitative scores based on the actions' respective
contribution to the team's victory. Our model is built using a sequence-based
deep learning model with a novel loss function working on the team match. The
sequence-based deep learning model process the action sequence from the game
start to the end of a player in a team play using a GRU unit that takes a
hidden state from the previous step and the current input selectively. The loss
function is designed to help the action score to reflect the final score and
the success of the team. We showed that our model can evaluate a player's
individual performance fairly and analyze the contributions of the player's
respective actions.
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