Ordered Policy Optimization
Overview
This paper introduces Ordered Policy Optimization (OPO), an improvement over Proximal Policy Optimization (PPO) that adapts clipping radii for extreme importance ratios based on their statistical reliability. By sorting importance ratios within minibatches and assigning adaptive sample-wise radii derived from a prescribed tail profile, OPO aims to control harmful updates more effectively than fixed clipping. Experiments on MuJoCo and Atari benchmarks show that this modification achieves strong performance relative to established baselines.
Generated from attributed reports · Updated 2 days ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalOrdered Policy Optimization
This paper introduces Ordered Policy Optimization (OPO), an improvement over Proximal Policy Optimization (PPO) that adapts clipping radii for extreme importance ratios based on their statistical reliability. By sorting importance ratios within minibatches and assigning adaptive sample-wise radii derived from a prescribed tail profile, OPO aims to control harmful updates more effectively than fixed clipping. Experiments on MuJoCo and Atari benchmarks show that this modification achieves strong performance relative to established baselines.
Event coverage history
Current attention 3·Peak within the comparable range 4（2026-11-10 12:00 UTC）·Change within the comparable range over 24 hours –
The trend compares participants observed continuously; its scope may be narrower than current attention totals. Hover or click to explore hourly observations, or use the left and right arrow keys.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).