Quality-Diversity Algorithms vs. PPO Robustness Under Adversarial Perturbations in Continuous Control

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How does the robustness of policies evolved via quality-diversity algorithms compare to standard PPO when evaluated under adversarial perturbations in continuous control tasks. The increasing importance of robots and automation creates a demand for learnable controllers which can be obtained through various approaches such as Evolutionary Algorithms (EAs) or Reinforcement Learning (RL). Unfortunately, these two families of algorithms have mainly. 15 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the robustness of policies evolved via quality-diversity algorithms compare to standard PPO when evaluated under adversarial perturbations in continuous control tasks?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance