Multi-Agent Reinforcement Learning (MARL) is a branch of machine learning where multiple agents learn to make decisions by interacting with an environment and each other. It extends the single-agent reinforcement learning paradigm to scenarios involving multiple decision-makers, each with their own objectives, which can lead to complex dynamics such as cooperation, competition, and negotiation.

• Robotics
• Autonomous vehicles
• Multi-agent systems
• Game playing
• Recommendation systems

A group of self-driving cars must navigate through a busy city street while avoiding collisions and coordinating their movements to minimize traffic congestion. Each car has its own reinforcement learning agent that learns to make decisions based on the environment and other cars’ actions.

• Generative AI in a company environment