Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE)

3 Jun 2019 Vahid Behzadan William Hsu

This paper investigates the effectiveness of adversarial training in enhancing the robustness of Deep Q-Network (DQN) policies to state-space perturbations. We first present a formal analysis of adversarial training in DQN agents and its performance with respect to the proportion of adversarial perturbations to nominal observations used for training... (read more)

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