Altahhan, A orcid.org/0000-0003-1133-7744 (2020) True Online TD(λ)-Replay An Efficient Model-free Planning with Full Replay. In: 2020 International Joint Conference on Neural Networks (IJCNN). 2020 International Joint Conference on Neural Networks (IJCNN), 19-24 Jul 2020, Glasgow, Scotland, United Kingdom. IEEE ISBN 978-1-7281-6927-9
Abstract
In this paper, we present a new reinforcement learning prediction method that extends the capabilities of the true online TD(λ) to allow an agent to efficiently replay all of its past experience, online in the sequence that they appear with. We demonstrate that, for problems that benefit from experience replay, our new method outperforms true online TD(λ), albeit quadratic in complexity due to its replay capabilities. In addition, we demonstrate that our method outperforms other methods with similar quadratic complexity such as Dyna Planning and TD(0)-Replay algorithms. We showcase the capabilities of our method on two benchmarking domains, a random walk problem tested with simple binary features and on a myoelectric domain that is tested with features that are deeply extracted from sEMG signals. Experimental results confirm the particular suitability of this method for a deep architecture over other methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Nov 2020 13:06 |
Last Modified: | 24 Nov 2020 13:06 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ijcnn48605.2020.9206608 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168203 |