AI RESEARCH
Distributional Value Estimation Without Target Networks for Robust Quality-Diversity
arXiv CS.LG
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ArXi:2604.20381v1 Announce Type: new Quality-Diversity (QD) algorithms excel at discovering diverse repertoires of skills, but are hindered by poor sample efficiency and often require tens of millions of environment steps to solve complex locomotion tasks. Recent advances in Reinforcement Learning (RL) have shown that high Update-to-Data (UTD) ratios accelerate Actor-Critic learning. While effective, standard high-UTD algorithms typically utilise target networks to stabilise