@inproceedings{a198d82e8786443280c7823d8e82fbc0,
title = "Forward Model Learning for Motion Control Tasks",
abstract = "In this work, we study the capabilities and limitations of forward model learning agents and their applications to motion-control tasks. Forward model learning agents learn to approximate the environment dynamics to apply planning algorithms for action-selection. While previous work has shown that forward model learning agents can efficiently learn to play simple video games, we extend their applicability to domains with continuous state and action spaces. Our experiments show that such agents are quickly able to learn an approximate model of their environment, which suffices to solve several simple motion-control tasks. Comparisons with deep reinforcement learning further highlight the sample efficiency of forward model learning agents.",
keywords = "Deep Reinforcement Learning, Forward Model Learning, Motion Control Tasks",
author = "Alexander Dockhorn and Rudolf Kruse",
year = "2020",
month = aug,
doi = "10.1109/IS48319.2020.9199978",
language = "English",
series = "2020 IEEE 10th International Conference on Intelligent Systems, IS 2020 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "551--556",
editor = "Vassil Sgurev and Vladimir Jotsov and Rudolf Kruse and Mincho Hadjiski",
booktitle = "2020 IEEE 10th International Conference on Intelligent Systems, IS 2020 - Proceedings",
address = "United States",
note = "10th IEEE International Conference on Intelligent Systems, IS 2020 ; Conference date: 28-08-2020 Through 30-08-2020",
}