A Robust $\widetilde{\mathcal{O}}(1/\sqrt{T})$ Rate for Unprojected TD Learning with Linear Function Approximation
arXiv:2506.01052v3 Announce Type: replace Abstract: We investigate the finite-time convergence properties of Temporal Difference (TD) learning with linear function approximation, a cornerstone of reinforcement learning. We are interested in the so-called ``robust'' setting, where the convergence guarantee does not depend on the potential function's minimal curvature. While prior work has established convergence guarantees in this setting, these results typically rely on the artificial assumption that each iterate is projected onto a bounded set. Removing such a condition was left as an open pr
The continuous academic advancements in reinforcement learning are incrementally improving core algorithms, with this paper addressing a long-standing theoretical limitation in TD learning.
Improved theoretical guarantees for TD learning without artificial assumptions can accelerate the development and reliability of AI systems, particularly in reinforcement learning applications.
The theoretical underpinnings of some reinforcement learning algorithms are becoming more robust, potentially leading to more stable and efficient practical implementations in complex environments.
- · AI/ML researchers
- · Developers of autonomous systems
- · Reinforcement learning applications sector
- · AI models relying on less robust TD learning methods
More reliable and less computationally intensive reinforcement learning agents could be developed.
This could enable applications in areas requiring high stability, such as robotics or complex control systems.
It might contribute to the broader advancement of AI agents, making them more capable in real-world, unconstrained environments.
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Read at arXiv cs.LG