… explicit finite time analysis of temporal difference learning with linear function approximation.
… and explicit finite time analysis of temporal difference learning. We draw inspiration from the …

… of a decentralized linear function approximation variant of the vanilla TD(0) learning, for …
We proved that such decentralized TD(0) algorithms converge linearly to a small neighborhood …

… TD with function approximation used for our analysis. In Section 3, we describe the tail-averaged
TD algorithm, and also present the finite time … in a TD algorithm, and provide finite time …

… we study a distributed variant of the temporaldifference learning method for solving the policy
evaluation problem in multi-agent reinforcement learning… , followed by a local TD(λ) update. …

… of a Markov chain using linear function approximators. The algorithm we … P and that at
time t the parameter vector r has been set to some value rt. We define the temporal difference dt …

… of the TD(0) learning algorithm with linear function approximation that we term AdaTD (0). In
contrast to the TD(0)… Singal, “A finite time analysis of temporal difference learning with linear …

… Summary: This chapter considers temporal difference algorithms within the context of
infinite-horizon finite… discounted cost and linear cost function approximation. This problem arises …

… and asymptotic approximation error of temporal-difference learning algorithms … linear function
approximation are analyzed. The analysis is carried out in the context of the approximation …

… We have introduced two new gradient-based temporaldifference learning algorithms … linear
function approximation in a general setting that includes both on-policy and off-policy learning…

… be practical to approximate the value of each state individually. Here we consider linear
function approximation, in which … The approximation to the value function is then required to be …