ANO Algorithm Enhances Robust Policy Optimization in Deep RL
Researchers introduce Anchored Neighborhood Optimization (ANO), a novel algorithm addressing the fundamental dilemma in Proximal Policy Optimization (PPO) where hard clipping discards gradient information from outliers, causing sample inefficiency. Removing clipping, as in SPO, leads to unbounded gradients and instability. ANO is derived from a Unified Trust Region Framework and introduces the Redescending Influence Principle, which dynamically suppresses outliers instead of monotonic penalties or hard-thresholding. Theoretically, ANO proves stability in high-variance stochastic optimization. The paper is available on arXiv with ID 2605.02320.
Key facts
- ANO stands for Anchored Neighborhood Optimization.
- PPO's hard clipping causes sample inefficiency by discarding gradient information from outliers.
- SPO removes clipping but leads to unbounded gradients and instability.
- A Unified Trust Region Framework generalizes existing objectives.
- The Redescending Influence Principle shifts from monotonic penalties and hard-thresholding to dynamic outlier suppression.
- ANO is proven to be necessary for stability in high-variance stochastic optimization.
- The paper is published on arXiv with ID 2605.02320.
- The research addresses a fundamental dilemma in deep reinforcement learning.
Entities
Institutions
- arXiv