Temporal-difference learning with linear features is being reexamined through the lens of stochastic differential equations, providing a more nuanced understanding of the asymptotic behavior of this core policy evaluation method. By introducing a diffusion approximation, researchers can better capture the stochastic fluctuations that govern the error floor in linear TD(0) under Markovian noise1. This advancement has significant implications for the field of reinforcement learning, as it enables more accurate modeling of complex systems and improved policy evaluation. The traditional ordinary differential equation description is limited in its ability to account for stochastic fluctuations, whereas the proposed SDE approximation offers a more comprehensive framework. This breakthrough matters to practitioners because it enhances the reliability and robustness of policy evaluation methods, ultimately informing more effective decision-making in dynamic environments.
A Diffusion Approximation for Temporal-Difference Learning with Linear Features under Markovian Noise
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References
- Authors. (2026, June 16). A Diffusion Approximation for Temporal-Difference Learning with Linear Features under Markovian Noise. arXiv. https://arxiv.org/abs/2606.18183v1
Original Source
arXiv ML
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