@inproceedings{harries2019mazeexplorer,
author = {Harries, Luke and Lee, Sebastian and Rzepecki, Jaroslaw and Hofmann, Katja and Devlin, Sam},
title = {MazeExplorer: A Customisable 3D Benchmark for Assessing Generalisation in Reinforcement Learning},
booktitle = {IEEE Conference on Games},
year = {2019},
month = {August},
abstract = {This paper presents a customisable 3D benchmark for assessing generalisability of reinforcement learning agents based on the 3D first-person game Doom and open source environment VizDoom. As a sample use-case we show that different domain randomisation techniques during training in a key-collection navigation task can help to improve agent performance on unseen evaluation maps.},
url = {http://approjects.co.za/?big=en-us/research/publication/mazeexplorer-a-customisable-3d-benchmark-for-assessing-generalisation-in-reinforcement-learning/},
}