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New Regularized Emphatic TD Learning Method Enhances Stability

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Regularized Emphatic Temporal-Difference Learning (RETD), a novel approach to stabilize temporal-difference learning updates. Unlike existing methods, RETD addresses issues with constant step sizes by introducing a normalized first-order repair mechanism. This method stores the emphatic TD signal in a leaky scalar state and releases a delayed correction, which has been validated through extensive experiments showing improved stability and exact recovery of the ETD fixed point. AI

RANK_REASON The cluster contains a single academic paper detailing a new learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Regularized Emphatic TD Learning Method Enhances Stability

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The cluster contains a single academic paper detailing a new learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xingguo Chen, Zhaohui Wu, Jinguo Ye, Chao Li, Shangdong Yang, Guang Yang, Skylar Liang, Wenhao Wang ·

    Regularized Emphatic Temporal-Difference Learning: Stability under Constant Stepsizes

    arXiv:2609.19170v1 Announce Type: new Abstract: Emphatic temporal-difference learning (ETD) stabilizes the expected off-policy TD update and changes its projection geometry, but neither property determines constant-stepsize sampled dynamics. We construct an ergodic two-state coun…

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