Behavior-Induced Mirror-Prox Temporal-Difference Learning for Faster Off-Policy Prediction
📰 ArXiv cs.AI
Learn how to improve off-policy prediction using Behavior-Induced Mirror-Prox Temporal-Difference Learning for faster and more stable results
Action Steps
- Implement Behavior-Induced Mirror-Prox Temporal-Difference Learning using linear function approximation
- Use behavior-policy transition information to update the geometry of the auxiliary-variable metric
- Compare the performance of the proposed method with existing Mirror-Prox TD methods
- Apply the method to off-policy prediction tasks with large state and action spaces
- Evaluate the stability and convergence of the proposed method using simulated environments
Who Needs to Know This
Researchers and engineers working on reinforcement learning and off-policy prediction can benefit from this method to improve the performance of their models
Key Insight
💡 Using behavior-policy transition information can provide a more informative update geometry for off-policy prediction
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🚀 Improve off-policy prediction with Behavior-Induced Mirror-Prox TD Learning! 🤖
Key Takeaways
Learn how to improve off-policy prediction using Behavior-Induced Mirror-Prox Temporal-Difference Learning for faster and more stable results
Full Article
Title: Behavior-Induced Mirror-Prox Temporal-Difference Learning for Faster Off-Policy Prediction
Abstract:
arXiv:2605.28849v1 Announce Type: new Abstract: Gradient temporal-difference methods provide stable off-policy prediction with linear function approximation, but their practical performance is strongly affected by the geometry induced by the auxiliary-variable metric. Existing Mirror-Prox TD methods typically use the feature covariance metric, whereas hybrid TD methods suggest that behavior-policy transition information can provide a more informative update geometry. This paper proposes a behavi
Abstract:
arXiv:2605.28849v1 Announce Type: new Abstract: Gradient temporal-difference methods provide stable off-policy prediction with linear function approximation, but their practical performance is strongly affected by the geometry induced by the auxiliary-variable metric. Existing Mirror-Prox TD methods typically use the feature covariance metric, whereas hybrid TD methods suggest that behavior-policy transition information can provide a more informative update geometry. This paper proposes a behavi
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