An Easy Introduction to Multi-Agent Reinforcement Learning
A tool to perform actions in a Collaborative fashion and achieve greater rewards or solve more complex tasks together faster
Not only just Autonomous Cars but Datacenters, Traffic Light Control Systems, Healthcare domains, Image Processing Fields, Robotics, Natural Language Processing, Gaming (Everyone knows about AlphaGo), and even Marketing, etc., etc. have been using Reinforcement Learning now to help them generate greater rewards or greater accuracy. So we can comfortably say that in the recent decade, the advancement in the field of Reinforcement Learning has been outstanding.
Now a sub-field of Reinforcement Learning is making great noise for researchers. People in the industry want this field to be better and better to complete their tasks in a much more efficient time and with better accuracy. That sub-field is Multi-Agent Reinforcement Learning.
Multi-Agent Reinforcement Learning
It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. Each agent is using some form of Reinforcement Learning to update their policy over time
