Multiagent Reinforcement Learning (Glossary)

The term Multi-Agent Reinforcement Learning belongs to the Artificial Intelligence category and holds an important place in automation, as well as industry and Industry 4.0.

Multi-agent reinforcement learning describes a method where several „agents“ – which could be robots or software programs, for example – learn how to solve tasks better through collaboration or competition. Each of these agents gathers its own experiences, makes decisions based on rewards (e.g. for correct behaviour) and thus develops its own strategies. The goal: all agents constantly improve through interaction.

A vivid example is the control of moving robots in a warehouse. Instead of training one robot alone, they all learn simultaneously – and pay attention not only to their own goals but also to the actions of others. This gradually results in optimised, coordinated behaviour: the robots efficiently avoid each other, share tasks, and collectively increase productivity.

In brief: Multi-agent reinforcement learning helps to create systems where many „players“ cooperate autonomously and intelligently, thereby optimising complex processes.