An Easy Introduction to Multi-Agent Reinforcement Learning

MARL models offer tangible benefits to deep learning tasks given that they are the closest representations of many cognitive activities in the real world. However, there are plenty of challenges to consider when implementing this type of model. Typically, MARL models use some training policy coordination mechanisms to minimize the impact of the training tasks. Imagine a multiplayer game in which two agents occupied the exact same position in the environment. To handle those challenges, the policy of each agent needs to take into account the actions taken by other agents.