New RETD algorithm enhances stability in emphatic temporal-difference learning

VERA Brief

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Researchers have developed a new algorithm called Regularized Emphatic Temporal-Difference Learning (RETD) to stabilize sampled dynamics in emphatic temporal-difference learning (ETD). This addresses limitations in ETD, particularly in constant-stepsize scenarios, and aims to improve the stability of learning processes.

Key facts

• Regularized Emphatic Temporal-Difference Learning (RETD) is a new algorithm designed to enhance stability in sampled dynamics within emphatic temporal-difference learning (ETD).
• Existing ETD methods have shown limitations in constant-stepsize sampled dynamics, where they can exhibit instability.
• RETD incorporates a normalized first-order post-shock repair mechanism to stabilize the learning process.
• Theoretical analysis indicates that RETD achieves almost-sure convergence for harmonic diminishing stepsizes and a conditional constant-stepsize moment-contraction.
• Experimental results on a two-state construction and a Baird point validate RETD's stability improvements compared to ETD.