Core Concepts
Optimizing regret in adversarial RL enhances robustness against observation attacks.
Abstract
This content delves into the concept of regret-based defense in adversarial reinforcement learning. It explores the vulnerabilities of deep reinforcement learning policies to adversarial noise in observations and proposes methods to enhance robustness. The analysis covers the importance of regret optimization, the formulation of regret-based defense approaches, and the comparison of these approaches with existing methods through empirical results on various benchmarks.
Abstract:
Deep Reinforcement Learning (DRL) policies are susceptible to adversarial noise in observations.
Existing approaches focus on regularization and maximin notions of robustness.
This study introduces regret-based defense to optimize robustness against adversarial attacks.
Introduction:
DRL models excel in complex tasks but are vulnerable to attacks on input.
Adversarial perturbations can lead to catastrophic consequences in safety-critical environments.
The study aims to develop inherently robust algorithms to counter observation-perturbing adversaries.
Regret-Based Adversarial Defense (RAD):
Defines regret and introduces Cumulative Contradictory Expected Regret (CCER) for scalable optimization.
Proposes RAD-DRN using value iteration and RAD-PPO using policy gradients to minimize CCER.
Introduces RAD-CHT, a cognitive hierarchy theory-based approach for adversary-reactive frameworks.
Experimental Results:
Evaluates RAD approaches against leading methods on MuJoCo, Atari, and Highway benchmarks.
Demonstrates superior robustness of RAD methods against various attacks, including strategic adversaries.
Compares performance degradation of approaches under increasing attack intensity.
Stats
Deep Reinforcement Learning (DRL) policies are vulnerable to adversarial noise in observations.
Regularization approaches aim to make expected value objectives robust by adding adversarial loss terms.
Maximin objectives focus on maximizing the minimum value for robustness.
The study introduces regret-based defense to optimize robustness against adversarial attacks.
Quotes
"We focus on optimizing a well-studied robustness objective, namely regret."
"Our methods outperform existing best approaches for adversarial RL problems across a variety of standard benchmarks."