Quality-Diversity Neuroevolution vs. Gradient-Based RL in MuJoCo Locomotion Benchmarks

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How do quality-diversity neuroevolution algorithms compare to gradient-based RL methods in terms of sample efficiency and final reward on MuJoCo locomotion benchmarks. Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for their simplicity and efficiency, but are consequently susceptible to divergence. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do quality-diversity neuroevolution algorithms compare to gradient-based RL methods in terms of sample efficiency and final reward on MuJoCo locomotion benchmarks?

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