Multi-Agent Reinforcement Learning (MARL)
Many learning agents in one shared environment. Non-stationarity, credit assignment, and the CTDE principle; cooperative, competitive and mixed games; QMIX, MAPPO, and self-play that produced superhuman teaming.
01.The Problem: Everyone Is Learning At Once
Everything you learned about RL so far assumed one learner alone in an environment. A single agent, a fixed game, a stationary world: the rules of the game do not change while you play it (the Markov transition table P stays put).
Now add more learners.
Think of your commute. Every driver on the road is doing their own reinforcement learning: try a route, see how long it takes, adjust. The system that used to be "the road" is now "the road PLUS thousands of agents rewriting their strategies every morning."
So the ground moves:
What happens to your best route when everyone discovers it is the best route?
It jams. Your environment literally changed because the other agents changed. A strategy that earned 20 minutes yesterday earns 40 today. Same road, different world.
That is Multi-Agent Reinforcement Learning (MARL): many learning agents acting in one shared environment, where each agent's environment includes the other agents — who are also updating their policies underneath you.
Single-agent RL did not just get "a bit harder." Its core assumption — stationarity — is gone, and its convergence proofs go with it.
Multi-Agent Loop with CTDE 🤝
Multi-Agent Loop with CTDE 🤝
Several agents act simultaneously in a shared environment, so the environment is non-stationary from each agent's view (others are learning too). The winning recipe is Centralized Training / Decentralized Execution (CTDE): a central critic uses global info to learn, but at execution each actor uses only its own local observation.
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