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

arXiv: https://arxiv.org/abs/1906.01119 · HTML: https://mlsanity.com/paper/1906.01119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Deep-Attack over the Deep Reinforcement Learning](https://mlsanity.com/paper/2205.00807/) (2022)

## Abstract (arXiv)

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.