Al-Nima, R. and Han, Tingting and Al-Sumaidaee, S. and Chen, Taolue and Woo, W. (2021) Robustness and performance of Deep Reinforcement Learning. Applied Soft Computing 105 (107295), ISSN 1568-4946.
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Robustness and Performance of Deep Reinforcement Learning.pdf - Author's Accepted Manuscript Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (3MB) | Preview |
Abstract
Deep Reinforcement Learning (DRL) has recently obtained considerable attention. It empowers Reinforcement Learning (RL) with Deep Learning (DL) techniques to address various difficult tasks. In this paper, a novel approach called the \emph{Genetic Algorithm of Neuron Coverage} (GANC) is proposed. It is motivated for improving the robustness and performance of a DRL network. The GANC uses Genetic Algorithm (GA) to maximise the Neuron Coverage (NC) of a DRL network by producing augmented inputs. We apply this method in the self-driving car applications, where it is crucial to accurately provide a correct decision for different road tracking views. We evaluate our method on the SYNTHIA-SEQS-05 databases in four different driving environments. Our outcomes are very promising - the best driving accuracy reached 97.75% - and are superior to the state-of-the-art results.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | Deep Reinforcement Learning, Genetic Algorithm, Neuron Coverage, Road tracking |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Tingting Han |
| Date Deposited: | 31 Mar 2021 15:00 |
| Last Modified: | 09 Aug 2023 12:50 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/43509 |
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