Issue #110 RL Edwin Dong
Multi-Agent RL, Simply Explained
A single reinforcement learning (RL) agent learns in a closed feedback loop. It observes an environment, picks an action, and updates its policy based on the reward it receives.
The environment may be complex, but from the agent's point of view, it behaves consistently enough to learn useful patterns from.
Multi-agent reinforcement learning (MARL) is a field of RL that deals with environments that are not as stable, where multiple agents share and interact within the same environment.
In MARL, agents act in parallel in a single environment, trained on rewards that depend on what the other agents do.
Picture a self-driving car navigating an intersection, a robot-controlled warehouse, or traffic signals coordinating roads. These are all examples of MARL problems.
Most of MARL research (old and new) is about routing around a dynamic, complex environment without giving up what makes RL useful in the first place.
