  TD(0) is one of the most commonly used algorithms in reinforcement learning.
Despite this, there is no existing finite sample analysis for TD(0) with
function approximation, even for the linear case. Our work is the first to
provide such results. Existing convergence rates for Temporal Difference (TD)
methods apply only to somewhat modified versions, e.g., projected variants or
ones where stepsizes depend on unknown problem parameters. Our analyses obviate
these artificial alterations by exploiting strong properties of TD(0). We
provide convergence rates both in expectation and with high-probability. The
two are obtained via different approaches that use relatively unknown, recently
developed stochastic approximation techniques.
