  Independent reinforcement learning algorithms have no theoretical guarantees
for finding the best policy in multi-agent settings. However, in practice,
prior works have reported good performance with independent algorithms in some
domains and bad performance in others. Moreover, a comprehensive study of the
strengths and weaknesses of independent algorithms is lacking in the
literature. In this paper, we carry out an empirical comparison of the
performance of independent algorithms on four PettingZoo environments that span
the three main categories of multi-agent environments, i.e., cooperative,
competitive, and mixed. We show that in fully-observable environments,
independent algorithms can perform on par with multi-agent algorithms in
cooperative and competitive settings. For the mixed environments, we show that
agents trained via independent algorithms learn to perform well individually,
but fail to learn to cooperate with allies and compete with enemies. We also
show that adding recurrence improves the learning of independent algorithms in
cooperative partially observable environments.
