Multi-Agent Reinforcement Learning (MARL)

Multi-Agent Reinforcement Learning (MARL) studies learning and coordination among multiple autonomous agents sharing an environment. It addresses non-stationarity, scalability and coordination challenges via cooperative, competitive or mixed reward structures. MARL is applied in simulations, distributed control and multi-agent decision-making for complex dyn…

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In brief

Multi-agent reinforcement learning in brief

Multi-agent reinforcement learning (MARL) trains multiple learning agents whose decisions affect one another and whose objectives may be cooperative, competitive, or mixed.

Where does it come from?

MARL grew by combining multi-agent systems with reinforcement learning and extending reward-based learning to settings with several decision makers.

The mental model

Picture several players on one board: each sees part of the state, acts, and receives reward while the others keep learning.

Key building blocks

cooperation

Cooperative agents share an objective or reward.

reward

A multi-agent environment defines observations, actions, transitions, and rewards.

non-stationarity

Credit assignment connects a team outcome to individual actions.

When is it useful?

The concept supports understandable design and reliable operation; its limits and operational consequences must be checked in context.

Wikipedia: Multi-agent reinforcement learning Ray RLlib Dokumentation: Multi-Agent Training PettingZoo GitHub (Multi-Agent Environments)

Why is this building block relevant?

MARL describes learning and coordination methods for multiple autonomous agents in shared environments with cooperative or competitive objectives.

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External references

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Wikipedia: Multi-agent reinforcement learning Ray RLlib documentation: Multi-Agent Training PettingZoo GitHub (Multi-Agent Environments)

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Dependency · Depends on

(1)

Reinforcement Learning

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