Robustness and performance of Deep Reinforcement Learning

Raid Rafi Omar Al-Nima*, Tingting Han, Saadoon Awad Mohammed Al-Sumaidaee, Taolue Chen, Wai Lok Woo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Deep Reinforcement Learning (DRL) has recently obtained considerable attentions. It empowers Reinforcement Learning (RL) with Deep Learning (DL) techniques to address various difficult tasks. In this paper, a novel approach called the 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.
Original languageEnglish
Article number107295
Number of pages12
JournalApplied Soft Computing
Early online date16 Mar 2021
Publication statusPublished - 1 Jul 2021


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