  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 $\epsilon$-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
$\epsilon$-greedy and parameter-space noise exploration algorithms.
