  We investigate the problem of computation offloading in a mobile edge
computing architecture, where multiple energy-constrained users compete to
offload their computational tasks to multiple servers through a shared wireless
medium. We propose a multi-agent deep reinforcement learning algorithm, where
each server is equipped with an agent, observing the status of its associated
users and selecting the best user for offloading at each step. We consider
computation time (i.e., task completion time) and system lifetime as two key
performance indicators, and we numerically demonstrate that our approach
outperforms baseline algorithms in terms of the trade-off between computation
time and system lifetime.
