  We present the extension of the Remember and Forget for Experience Replay
(ReF-ER) algorithm to Multi-Agent Reinforcement Learning (MARL). ReF-ER was
shown to outperform state of the art algorithms for continuous control in
problems ranging from the OpenAI Gym to complex fluid flows. In MARL, the
dependencies between the agents are included in the state-value estimator and
the environment dynamics are modeled via the importance weights used by ReF-ER.
In collaborative environments, we find the best performance when the value is
estimated using individual rewards and we ignore the effects of other actions
on the transition map. We benchmark the performance of ReF-ER MARL on the
Stanford Intelligent Systems Laboratory (SISL) environments. We find that
employing a single feed-forward neural network for the policy and the value
function in ReF-ER MARL, outperforms state of the art algorithms that rely on
complex neural network architectures.
