  In this paper, a joint task, spectrum, and transmit power allocation problem
is investigated for a wireless network in which the base stations (BSs) are
equipped with mobile edge computing (MEC) servers to jointly provide
computational and communication services to users. Each user can request one
computational task from three types of computational tasks. Since the data size
of each computational task is different, as the requested computational task
varies, the BSs must adjust their resource (subcarrier and transmit power) and
task allocation schemes to effectively serve the users. This problem is
formulated as an optimization problem whose goal is to minimize the maximal
computational and transmission delay among all users. A multi-stack
reinforcement learning (RL) algorithm is developed to solve this problem. Using
the proposed algorithm, each BS can record the historical resource allocation
schemes and users' information in its multiple stacks to avoid learning the
same resource allocation scheme and users' states, thus improving the
convergence speed and learning efficiency. Simulation results illustrate that
the proposed algorithm can reduce the number of iterations needed for
convergence and the maximal delay among all users by up to 18% and 11.1%
compared to the standard Q-learning algorithm.
