Title: Joint-Predictive Representations for Multi-Agent Reinforcement Learning. Abstract: The recent advances in reinforcement learning have demonstrated the effectiveness of vision-based self-supervised learning (SSL). However, the main efforts on this direction have been paid on single-agent setting, making multi-agent reinforcement learning~(MARL) lags thus far. There are two significant obstacles that prevent applying off-the-shelf SSL approaches with MARL on a partially observable multi-agent system : (a) each agent only gets a partial observation, and (b) previous SSL approaches only take consistent temporal representations into account, while ignoring the characterization that captures the interaction and fusion among agents. In this paper, we propose \textbf{M}ulti-\textbf{A}gent  \textbf{Jo}int-Predictive \textbf{R}epresentations~(MAJOR), a novel framework to explore self-supervised learning on cooperative MARL. Specifically, we treat the latent representations of local observations of all agents as the sequence of masked contexts of the global state, and we then learn effective representations by predicting the future latent representations for each agent with the help of the agent-level information interactions in a joint transition model. We have conducted extensive experiments on wide-range MARL environments, including both vision-based and state-based scenarios, and show that our proposed MAJOR achieves superior asymptotic performance and sample efficiency against other state-of-the-art methods.