AI engineering glossary · Reinforcement learning
What is MARL (multi-agent reinforcement learning)?
Multi-agent reinforcement learning (also called MARL): Learning when several decision-makers act in the same world at once, each adapting to the others. From any single learner's view the world keeps changing, which makes learning harder. Settings can be cooperative, competitive or mixed.
Multi-agent reinforcement learning: an example
Four delivery bots learning to share a warehouse without blocking each other.
Related terms
More Reinforcement learning terms
- Multi-armed bandit
- MuZero
- n-step return
- Nash equilibrium
- Natural policy gradient
- Noisy networks
- Off-policy
- Off-policy evaluation
Where you learn it
Multi-agent reinforcement learning is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.