Reproduce “WarpSAC: Towards the Pinnacle of Scalable Off-policy RL by Rethinking Exploration and Exploitation” on OpenResearch
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About this paper
Zihao Wu, Hongyao Tang, Yi Ma, Huizhong Song, Pengyi Li, Yifu Yuan, Fei Ni, Jinyi Liu and 4 more
WarpSAC matches off-policy robot learning safeguards to data availability, reweighting replay and adjusting normalization and critic count. It reports about 35 minutes to train Unitree G1 Flat on one A800, versus about 55 minutes for FlashSAC. Its normalized score-step AUC is 4.5% higher across nine CPU-scale environments and 23.1% higher across fourteen GPU-parallel environments; these comparisons span different suites and setups.
Key insights
- The utility of classical off-policy RL stabilizers is data-regime dependent; mechanisms beneficial in data-limited settings can become restrictive in data-abundant environments.
- Parameter projection normalization and clipped double-Q targets, while useful for stability in data-scarce scenarios, can hinder efficient exploitation and expressive freedom in data-rich contexts.
- Sample Weight Decay (SWD) is a regime-agnostic stabilizer, consistently improving learning efficiency by concentrating updates on policy-relevant transitions across both data-limited and data-abundant settings.
Summary by alphaXiv.
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