Regularized Centered Emphatic Temporal Difference Learning
Off-policy temporal-difference (TD) learning with function approximation faces a structural tradeoff among stability, projection geometry, and variance control. Emphatic TD (ETD) improves the off-policy projection geometry through follow-on emphasis, but the follow-on trace can have high variance. We revisit this tradeoff through Bellman-error centering. Although centering naturally removes a common drift term from TD errors, we show that a naive centered emphatic extension introduces an auxiliary coupling that can destroy the positive-definiteness of the ETD key matrix. We propose \emph{Regularized Emphatic Temporal-Difference Learning} (RETD), which preserves the follow-on trace and reg...
| Authors | Xingguo Chen, Chaohui Wu, Jinguo Ye et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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