  In this paper, we study asynchronous stochastic approximation algorithms
without communication delays. Our main contribution is a stability proof for
these algorithms that extends a method of Borkar and Meyn by accommodating more
general noise conditions. We also derive convergence results from this
stability result and discuss their application in important average-reward
reinforcement learning problems.
