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arXiv CS.AI·1d ago·5 min read
Target-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling
TL;DR
Target-ESS Tempered Policy Optimization (TETPO) introduces a novel approach to stabilize off-policy reinforcement learning by adaptively controlling importance weights, significantly reducing variance in gradient estimates. This method outperforms existing baselines on mathematical reasoning tasks under high staleness conditions.
✦ Why It Matters
Implement TETPO in your reinforcement learning projects to enhance stability and performance under off-policy conditions.
Key Takeaways
- Adopt TETPO for better variance control in reinforcement learning.
- Use data-adaptive exponents to optimize importance weights.
- Monitor effective sample size to ensure stability in updates.
- Evaluate performance improvements on mathematical reasoning tasks.
How It Works
TETPO modifies the importance weights used in reinforcement learning by introducing a data-adaptive exponent. This exponent, ranging from 0 to 1, allows the method to smoothly transition between using original weights and applying uniform weights, depending on the concentration of weights.
By selecting the largest exponent that meets a target effective sample size (ESS) fraction, TETPO effectively manages the variance in gradient estimates, ensuring stability during learning.