Applying Hybrid Reward Architecture to a Fighting Game AI
Yoshina Takano, Wenwen Ouyang, Suguru Ito, Tomohiro Harada, Ruck Thawonmas · 2018
In this paper, we propose a method for implementing a competent fighting game AI using Hybrid Reward Architecture (HRA). In 2017, an AI using HRA developed by Seijen et al. achieved a perfect score of 999,990 in Ms. Pac-Man. HRA decomposes a reward function into multiple components and learns a separate value function for each component in the reward function. Due to reward decomposition, an optimal value function can be learned in the domain of Ms. Pac-Man. However, the number of actions in Ms. Pac-Man is only limited to four (Up, Down, Left, and Right), and till now whether HRA is also effective in other games with a larger number of actions is unclear. In this paper, we apply HRA and verify its effectiveness in a fighting game. For performance evaluation, we use FightingICE that has 40 actions and has been used as the game platform in the Fighting Game AI Competition at CIG since 2014. Our experimental results show that the proposed HRA AI, a new sample AI for the competition, is superior to non-HRA deep learning AIs and is competitive against other entries of the 2017 competition.