Social learning helps humans and animals rapidly adapt to new circumstances, and drives the emergence of complex learned behaviors. This tutorial focuses on Social Reinforcement Learning, RL algorithms that leverage multi-agent social learning to improve single-agent learning and generalization, multi-agent coordination, and human-AI interaction. We will cover how to use multi-agent training to generate a curriculum of increasingly complex learning tasks, driving agents to learn more complex behavior, and improving zero-shot transfer to unknown, single-agent test tasks. We will discuss how social learning from agents that are present in the environment can provide similar benefits, and enhance human-AI interaction. Finally, we will discuss the problem of learning to coordinate with other agents, review some of the key challenges, and introduce several proposed approaches. We will show how techniques like social influence, which maximizes mutual information between agents’ actions, can improve coordination without depending on assumptions like centralized training or shared rewards. The tutorial aims to demonstrate that multi-agent social learning—whether through competition, cooperation, or merely co-existence—can enhance RL agents’ ability to acquire interesting behavior, generalize to new environments, and interact with people.