Entropy-Preserving Reinforcement Learning

2026-03-30 · Source: Apple Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new paper, "Entropy-Preserving Reinforcement Learning," published in March 2026 by Aleksei Petrenko, Ben Lipkin, Kevin Chen, Erik Wijmans, Marco Cusumano-Towner, Raja Giryes, and Philipp Krähenbühl, addresses the issue of entropy reduction in policy gradient algorithms. The authors demonstrate that many leading policy gradient methods inherently decrease the diversity of explored trajectories during training, limiting a policy's exploratory capacity. They advocate for active monitoring and control of entropy throughout training, analyzing the impact of policy gradient objectives and empirical factors like numerical precision on entropy dynamics. The paper introduces REPO, a family of algorithms that modify the advantage function for entropy regulation, and ADAPO, an adaptive asymmetric clipping approach. These entropy-preserving methods result in policies that maintain diversity, achieve higher performance, and retain trainability for sequential learning in new environments.

Key takeaway

For research scientists developing or deploying policy gradient algorithms, you should actively monitor and control entropy during training to prevent the loss of exploratory diversity. Implementing methods like REPO or ADAPO can lead to more performant policies that are better equipped for sequential learning in novel environments, ensuring your models retain adaptability and robust exploration capabilities.

Key insights

Policy gradient algorithms often reduce exploration diversity; active entropy control improves performance and trainability.

Principles

• Entropy reduction limits policy exploration.
• Active entropy control enhances policy diversity.

Method

The REPO algorithm family modifies the advantage function to regulate entropy, while ADAPO uses adaptive asymmetric clipping to control entropy dynamics during policy gradient training.

In practice

• Implement REPO for entropy regulation.
• Apply ADAPO for adaptive clipping.

Topics

• Entropy-Preserving RL
• Policy Gradient Algorithms
• Entropy Control
• REPO Algorithm
• ADAPO

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Apple Machine Learning Research.