Abstract
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 language | English |
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Article number | 107295 |
Number of pages | 12 |
Journal | Applied Soft Computing |
Volume | 105 |
Early online date | 16 Mar 2021 |
DOIs | |
Publication status | Published - 1 Jul 2021 |
Keywords
- Deep Reinforcement Learning
- Genetic Algorithm
- Neuron Coverage
- Road tracking