Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning

Yuxin Wu; Yuandong Tian

ICLR 2017poster283 citations

Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning

Abstract

In this paper, we propose a novel framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom. Our framework combines the state-of-the-art reinforcement learning approach (Asynchronous Advantage Actor-Critic (A3C) model) with curriculum learning. Our model is simple in design and only uses game states from the AI side, rather than using opponents' information. On a known map, our agent won 10 out of the 11 attended games and the champion of Track1 in ViZDoom AI Competition 2016 by a large margin, 35\% higher score than the second place.

Reinforcement LearningApplicationsGames

BibTeX

@inproceedings{
wu2017training,
title={Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning},
author={Yuxin Wu and Yuandong Tian},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=Hk3mPK5gg}
}