Stabilizing Reinforcement Learning: Entropy Ratio Clipping as a Global Constraint
Published:Dec 5, 2025 10:26
•1 min read
•ArXiv
Analysis
This research explores a method to stabilize reinforcement learning algorithms using entropy ratio clipping. The paper likely investigates the performance of this method on various benchmarks and compares it to existing techniques.
Key Takeaways
- •Proposes a new approach to stabilizing reinforcement learning.
- •Utilizes entropy ratio clipping as a soft global constraint.
- •Potentially improves the robustness and stability of RL agents.
Reference
“The research focuses on using entropy ratio clipping.”