Robustness and performance of Deep Reinforcement Learning

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The paper introduces a novel approach called GANC to improve the robustness and performance of a DRL network by maximizing neuron coverage through augmented inputs. The method is applied in self-driving car applications, achieving very promising outcomes.