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cloud.google.com·7h ago
AI Research
arXiv CS.AI·24d ago·5 min read
Regularized Emphatic Temporal-Difference Learning: Stability under Constant Stepsizes
TL;DR
Regularized Emphatic Temporal-Difference Learning (RETD) enhances stability in off-policy learning by introducing a normalized repair mechanism, ensuring convergence even with constant stepsizes. This method effectively manages the dynamics of learning while maintaining the integrity of the emphatic TD signal.
✦ Why It Matters
Implement RETD in your reinforcement learning projects to enhance stability and convergence under constant stepsizes.
Key Takeaways
- Adopt RETD for improved stability in reinforcement learning.
- Utilize delayed corrections to manage learning dynamics effectively.
- Experiment with both diminishing and constant stepsizes.
- Validate your models with extensive run experiments to ensure robustness.
How It Works
RETD introduces a normalized first-order repair mechanism that maintains the trace and importance ratios while storing the emphatic TD signal in a leaky scalar state. This allows for delayed corrections that adjust the learning dynamics post-shock, ensuring stability even under constant stepsizes.