Multi-Agent Reinforcement Learning

Traffic lights are not stupid. They are obedient. That is the problem. A fixed-time signal does exactly what it was told to do: hold this green for this long, clear the junction, move to the next phase, repeat. It does not care that one lane is empty, another is spilling backward, and a third has just received a platoon of vehicles from the previous intersection. It is not being malicious. It is merely following a plan designed for a world that stopped changing five minutes ago. ...

Robots are easy to imagine as individuals. A quadruped walks. A drone flies. A warehouse arm picks. The business slide is usually kind enough to show one machine, one task, one satisfying arrow from input to output. Reality is less polite. A quadruped is not one decision-maker. It is a committee of limbs negotiating with gravity. A multi-drone system is not one policy with four propellers. It is a moving argument about timing, local perception, shared goals, and what not to crash into. A factory cell with multiple robotic agents is even worse: every local action changes the environment other agents are trying to understand. ...