A primal-dual perspective for distributed TD-learning
Han-Dong Lim Donghwan Lee
The goal of this paper is to investigate distributed temporal difference (TD)
learning for a networked multi-agent Markov decision process. The proposed
approach is based on distributed optimization algorithms, which can be
interpreted as primal-dual Ordinary differential equation (ODE) dynamics
subject to null-space constraints. Based on the exponential convergence
behavior of the primal-dual ODE dynamics subject to null-space constraints, we
examine the behavior of the final iterate in various distributed TD-learning
scenarios, considering both constant and diminishing step-sizes and
incorporating both i.i.d. and Markovian observation models. Unlike existing
methods, the proposed algorithm does not require the assumption that the
underlying communication network structure is characterized by a doubly
stochastic matrix.