Traiding Card Game Environments based on deep reinforcement learning

Abstract

Recently, the application of deep reinforcement learning to game environments has attracted attention. In particular, the game with imperfect information has been paid attention to in this field. In this research, we focus on the trading card game (TCG). TCG is more difficult to be played with artificial intelligence than other games because the performance and types of available cards can be changed. This nature also makes it difficult to adjust the game balance, and it is common for the game to be modified after its release, and terms such as "buff" to change the card performance upward and "nerf" to change it downward are used. Based on the above background, we propose game balance optimization methods for TCG environments using deep reinforcement learning and evolutionary computation and demonstrate the effectiveness of the proposed methods through numerical experiments using our own TCG environment.