Deep reinforcement learning has become an important paradigm for constructing\nagents that can enter complex multi-agent situations and improve their policies\nthrough experience. One commonly used technique is reactive training - applying\nstandard RL methods while treating other agents as a part of the learner's\nenvironment. It is known that in general-sum games reactive training can lead\ngroups of agents to converge to inefficient outcomes. We focus on one such\nclass of environments: Stag Hunt games. Here agents either choose a risky\ncooperative policy (which leads to high payoffs if both choose it but low\npayoffs to an agent who attempts it alone) or a safe one (which leads to a safe\npayoff no matter what). We ask how we can change the learning rule of a single\nagent to improve its outcomes in Stag Hunts that include other reactive\nlearners. We extend existing work on reward-shaping in multi-agent\nreinforcement learning and show that that making a single agent prosocial, that\nis, making them care about the rewards of their partners can increase the\nprobability that groups converge to good outcomes. Thus, even if we control a\nsingle agent in a group making that agent prosocial can increase our agent's\nlong-run payoff. We show experimentally that this result carries over to a\nvariety of more complex environments with Stag Hunt-like dynamics including\nones where agents must learn from raw input pixels.\n