  There is a prevalence of multiagent reinforcement learning (MARL) methods
that engage in centralized training. But, these methods involve obtaining
various types of information from the other agents, which may not be feasible
in competitive or adversarial settings. A recent method, the interactive
advantage actor critic (IA2C), engages in decentralized training coupled with
decentralized execution, aiming to predict the other agents' actions from
possibly noisy observations. In this paper, we present the latent IA2C that
utilizes an encoder-decoder architecture to learn a latent representation of
the hidden state and other agents' actions. Our experiments in two domains --
each populated by many agents -- reveal that the latent IA2C significantly
improves sample efficiency by reducing variance and converging faster.
Additionally, we introduce open versions of these domains where the agent
population may change over time, and evaluate on these instances as well.
