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 behavior-induced Mirror-Prox temporal-difference method, called STHTD-MP, which replaces the covariance me
This paper introduces a new method for improving off-policy prediction in gradient temporal-difference methods, building upon existing research in Mirror-Prox TD and hybrid TD approaches.
Improved off-policy prediction algorithms can lead to more stable and faster reinforcement learning systems, enhancing the capabilities of autonomous AI agents.
The proposed STHTD-MP method offers a potentially more efficient and stable way to train AI, particularly in scenarios requiring off-policy learning where data is collected under a different behavior policy.
- · AI researchers
- · Reinforcement learning developers
- · Autonomous systems
- · Less efficient off-policy prediction algorithms
Enhances the practical performance and robustness of reinforcement learning algorithms used in various AI applications.
Accelerates the development and deployment of sophisticated AI agents across industries by making their learning processes more efficient.
Contributes to the broader capabilities of AI, potentially enabling more complex and adaptive autonomous systems in the long run.
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Read at arXiv cs.AI