Consider a team of cooperative players that take actions in a networkedenvironment. At each turn, each player chooses an action and receives a reward that is an unknown function of all the players' actions. The goal of the team of players is to learn to play together the action profile that maximizes the sum of their rewards. However, players cannot observe the actions or rewards of other players, and can only get this information by communicating with their neighbors.

In this section, we conduct an ablation study analyzing the effects of iterations in our proposed approach. Moreover, we see that as the iterations progress, the 2D landmark prediction error continues to reduce as seen in Figure 1a. The red points represent the frames that were given initial 2D input labels. The colorbar of these scatter plots represents the reprojection error (Eq. Weassume that only asingle object of interest (Chimpanzee in Figure 1is visible in each frame.

Neural networks are known to be susceptible to adversarial samples: small variations of natural examples crafted to deliberately mislead the models.