# Does TD(0) prediction require Robbins-Monro conditions to converge to the value function?

Does the learning rate parameter $$\alpha$$ require the Robbins-Monro conditions below for the TD(0) algorithm to converge to the true value function of a policy?

$$\sum \alpha_t =\infty \quad \text{and}\quad \sum \alpha^{2}_t <\infty$$

• Thanks for that, I was wondering is it true that TD(0) converges to the value function in Expected value when $\alpha$ is constant? Is there a condition that $\alpha \in (0,1)$? Feb 24 '20 at 23:23
• The result is in Sutton's Learning to Predict by the Methods of Temporal Differences , on page 24 of this version. I think I understand the result now. It is saying that there is some range of alphas $\alpha \in (0,t)$ such that TD(0) converges to the value function in expected value. Feb 25 '20 at 0:05