This Obscure Area of Game Theory can Help to Scale Reinforcement Learning to Infinite Agents

Reinforcement learning is one of the most popular areas of research in deep learning nowadays. Part of the popularity of reinforcement learning is due to the fact that is one of the learning methods that resembles human cognition the closets. In reinforcement learning scenarios and agent learns organically by taking actions on an environment and receiving specific rewards. A little less known discipline called multi-agent reinforcement learning(MARL) focuses on reinforcement learning scenarios involving a large number of agents. Typically, MARL scenarios suffer from a scalability challenges in which its complexity increases linearly with the number of agents in the environment.