  This paper extends off-policy reinforcement learning to the multi-agent case
in which a set of networked agents communicating with their neighbors according
to a time-varying graph collaboratively evaluates and improves a target policy
while following a distinct behavior policy. To this end, the paper develops a
multi-agent version of emphatic temporal difference learning for off-policy
policy evaluation, and proves convergence under linear function approximation.
The paper then leverages this result, in conjunction with a novel multi-agent
off-policy policy gradient theorem and recent work in both multi-agent
on-policy and single-agent off-policy actor-critic methods, to develop and give
convergence guarantees for a new multi-agent off-policy actor-critic algorithm.
