Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE)
Year: 2019
Type: preprint
Abstract: 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. Next, we consider the sample-inefficiency of current adversarial training techniques, and propose a novel Adversarially-Guided Exploration (AGE) mechanism based on a modified hybrid of the $ε$-greedy algorithm and Boltzmann exploration. We verify the feasibility of this exploration mechanism through experimental evaluation of its performance in comparison with the traditional decaying $ε$-greedy and parameter-space noise exploration algorithms.
Source: arXiv (Cornell University)
Authors: Behzadan, Vahid, Hsu, William
Institution: Kansas State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2948939059