Fast and Robust Convergence Rate for TD(0) with Linear Function Approximation, Universal Learning Steps and I.I.D. Samples
arXiv:2606.05967v1 Announce Type: cross Abstract: In this paper, we study the finite-time behavior of the TD(0) temporal-difference method with linear function approximation (LFA). We consider on-policy independent and identically distributed (i.i.d.) samples, a constant learning step, and the Polyak-Juditsky averaging method. We establish a new convergence rate, for the Mean-Square Error (MSE) on the approximated function, that is (i) fast in the sense that it admits an optimal dependency in the number of iterations k (i.e., of order 1/k), (ii) robust to ill-conditioning: it only depends on a
This research provides advancements in the theoretical understanding and practical convergence of reinforcement learning algorithms, a core component of modern AI systems.
Improved convergence rates and robustness for TD(0) with linear function approximation can lead to more efficient and reliable AI agent training, impacting various applications from robotics to autonomous decision-making.
The theoretical understanding of optimal learning steps and convergence for certain reinforcement learning methods is enhanced, potentially accelerating practical AI development by providing more dependable algorithms.
- · AI researchers
- · Reinforcement learning developers
- · Tech companies developing AI agents
More stable and faster training of AI models using TD(0) with linear function approximation.
Accelerated development and broader adoption of AI agents in various industries due to increased reliability and efficiency.
Potentially enables more complex and robust autonomous systems by improving foundational AI learning algorithms.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG