Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction
📰 ArXiv cs.AI
Learn to stabilize off-policy temporal-difference learning with behavior-aware auxiliary corrections for improved prediction accuracy
Action Steps
- Implement TDC to stabilize off-policy TD learning
- Apply TDRC to regularize the auxiliary correction
- Replace the auxiliary covariance geometry with a behavior-aware approach
- Evaluate the performance of the behavior-aware correction using linear prediction settings
- Compare the results with traditional TDC and TDRC methods
Who Needs to Know This
Researchers and engineers working on reinforcement learning and temporal-difference prediction can benefit from this article to improve the stability of their models
Key Insight
💡 Behavior-aware auxiliary corrections can improve the stability of off-policy temporal-difference learning
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🤖 Improve off-policy TD learning stability with behavior-aware auxiliary corrections! 📈
Key Takeaways
Learn to stabilize off-policy temporal-difference learning with behavior-aware auxiliary corrections for improved prediction accuracy
Full Article
Title: Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction
Abstract:
arXiv:2605.28855v1 Announce Type: new Abstract: Temporal-difference learning with function approximation can be unstable under off-policy sampling. TDC stabilizes off-policy TD through an auxiliary covariance correction, and TDRC further regularizes this correction in a single-timescale recursion. This paper studies a behavior-aware replacement of the auxiliary covariance geometry in the linear prediction setting, which is the standard local model for understanding the feature-space dynamics of
Abstract:
arXiv:2605.28855v1 Announce Type: new Abstract: Temporal-difference learning with function approximation can be unstable under off-policy sampling. TDC stabilizes off-policy TD through an auxiliary covariance correction, and TDRC further regularizes this correction in a single-timescale recursion. This paper studies a behavior-aware replacement of the auxiliary covariance geometry in the linear prediction setting, which is the standard local model for understanding the feature-space dynamics of
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