The regenerative potential of skeletal muscle declines with age, and this impairment is associated with an increase in tissue fibrosis. We show that muscle stem cells (satellite cells) from aged mice tend to convert from a myogenic to a fibrogenic lineage as they begin to proliferate and that this conversion is mediated by factors in the systemic environment of the old animals. We also show that this lineage conversion is associated with an activation of the canonical Wnt signaling pathway in aged myogenic progenitors and can be suppressed by Wnt inhibitors. Furthermore, components of serum from aged mice that bind to the Frizzled family of proteins, which are Wnt receptors, may account for the elevated Wnt signaling in aged cells. These results indicate that the Wnt signaling pathway may play a critical role in tissue-specific stem cell aging and an increase in tissue fibrosis with age.

We present four new reinforcement learning algorithms based on actor-critic, function approximation, and natural gradient ideas, and we provide their convergence proofs. Actor-critic reinforcement learning methods are online approximations to policy iteration in which the valuefunction parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their compatibility with function approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of special interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further reduce variance in some cases. Our results extend prior two-timescale convergence results for actor-critic methods by Konda and Tsitsiklis by using temporal difference learning in the actor and by incorporating natural gradients. Our results extend prior empirical studies of natural actor-critic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms. We present empirical results verifying the convergence of our algorithms.