Solving complex real-world tasks, e.g., autonomous fleet control, often involves a coordinated team of multiple agents which learn strategies from visual inputs via reinforcement learning. Many existing multi-agent reinforcement learning (MARL) algorithms however don’t scale to environments where agents operate on visual in-puts. To address this issue, algorithmically, recent works have focused on non-stationarity, exploration or communication. In contrast, we study whether scalability can also be achieved via a dis-entangled representation. For this, we explicitly construct an object-centric intermediate representation to characterize the states of an environment, which we refer to as ‘semantic tracklets.’ We evaluate ‘semantic tracklets’ on the visual multi-agent particle environment (VMPE) and on the challenging visual multi-agent GFootball environment. ‘Semantic tracklets’ consistently outperform baselines on VMPE, and achieve a +2.4 higher score difference than baselines on GFootball. Notably, this method is the first to success-fully learn a strategy for five players in the GFootball environment using only visual data.