What is Multi-agent reinforcement learning?

Multi-agent reinforcement learning is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple agents that coexist in a shared environment. Each agent is motivated by its own rewards, and does actions to advance its own interests; these interests may be opposed to the interests of other agents, resulting in complex group dynamics.

Multi-agent reinforcement learning is closely related to game theory and especially repeated games. Its study combines the pursuit of finding ideal algorithms that maximize rewards with a more sociological set of concepts. While research in single-agent reinforcement learning is concerned with finding the algorithm that gets the biggest number of points for one agent, research in multi-agent reinforcement learning evaluates and quantifies social metrics, such as cooperation, reciprocity, equity, social influence, language and discrimination.