Playing FPS Games With Environment-Aware Hierarchical Reinforcement Learning
Shihong Song# Jiayi Weng# Hang Su Dong Yan Haosheng Zou Jun Zhu
Tsinghua University      #The authors contributed equally to this work.


Learning rational behaviors in First-person-shooter (FPS) games is a challenging task for Reinforcement Learning (RL) with the primary difficulties of huge action space and insufficient exploration. To address this, we propose a hierarchical agent based on combined options with intrinsic rewards to drive exploration. Specifically, we present a hierarchical model that works in a manager-worker fashion over two levels of hierarchy. The high-level manager learns a policy over options, and the low-level workers, motivated by intrinsic reward, learn to execute the options. Performance is further improved with environmental signals appropriately harnessed. Extensive experiments demonstrate that our trained bot significantly outperforms the alternative RL-based models on FPS games requiring maze solving and combat skills, etc. Notably, we achieved first place in VDAIC 2018 Track(1).


You can download full paper through IJCAI site.

Bibtex Reference
author = {Shihong Song and Jiayi Weng and Hang Su and Dong Yan and Haosheng Zou and Jun Zhu},
title = {Playing {FPS} Games With Environment-Aware Hierarchical Reinforcement Learning},
booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI} 2019, Macao, China, August 10-16, 2019},
pages = {3475--3482},
year = {2019},
url = {},
doi = {10.24963/ijcai.2019/482},
timestamp = {Thu, 19 Sep 2019 14:00:20 +0200},
biburl = {},
bibsource = {dblp computer science bibliography,}

Video demos

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