# Multi-agent reinforcement learning

> sub-field of reinforcement learning

**Wikidata**: [Q85786957](https://www.wikidata.org/wiki/Q85786957)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Multi-agent_reinforcement_learning)  
**Source**: https://4ort.xyz/entity/multi-agent-reinforcement-learning

## Summary
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning, which is a type of machine learning. In this framework, agents learn how to behave in an environment by performing actions and receiving rewards or penalties, aiming to maximize cumulative reward over time.

## Key Facts
- **Classification:** Subclass of reinforcement learning.
- **Aliases:** Also known as multi-agent learning and MARL.
- **Core Mechanism:** Agents learn by performing actions and receiving feedback in the form of rewards or penalties.
- **Goal:** To maximize cumulative reward over time.
- **Wikipedia Presence:** The topic has sitelinks across 6 languages (ca, en, es, fa, he, uk).
- **Google Knowledge Graph ID:** /g/11fpjwxwbb.
- **Parent Field:** Reinforcement learning (sitelink count: 42).

## FAQs
### Q: What is Multi-agent reinforcement learning?
A: Multi-agent reinforcement learning (MARL) is a specific sub-field of reinforcement learning. It involves agents learning to navigate and behave within an environment by executing actions and optimizing for cumulative rewards.

### Q: What are the common aliases for this field?
A: The field is frequently referred to by the acronym MARL or simply as multi-agent learning.

### Q: How does the learning process work in this context?
A: As a subset of reinforcement learning, the process involves an agent learning through interaction with an environment. The agent performs actions and receives rewards or penalties in return, adjusting its behavior to maximize the total reward over time.

## Why It Matters
Multi-agent reinforcement learning represents a specialized subdivision of machine learning focused on the methodology of reward-based learning. Its significance lies in its structural role within the broader category of reinforcement learning, which is defined by the interaction between an agent and its environment. By categorizing specific learning problems under this sub-field, researchers and systems can better define scenarios where agents must optimize behavior based on cumulative feedback—a fundamental concept in automated decision-making and intelligent system design.

## Notable For
- Being a distinct **sub-field** of the broader reinforcement learning discipline.
- Operationalizing the concept of **cumulative reward maximization**.
- Possessing a distinct identifier in the **Google Knowledge Graph** (/g/11fpjwxwbb).
- Having a multilingual presence on Wikipedia, including English, Spanish, and Hebrew.

## Body

### Definition and Classification
Multi-agent reinforcement learning (MARL) is formally classified as a subclass of **reinforcement learning**. It inherits the fundamental properties of its parent field, defined as a type of machine learning where an agent learns how to behave in an environment. The learning process is driven by the agent performing actions and receiving rewards or penalties in return.

### Mechanism of Action
The primary objective within this field is the maximization of **cumulative reward over time**. Unlike other learning paradigms that might rely on static datasets, this approach focuses on dynamic interaction. The agent must navigate the environment, making decisions that balance immediate feedback with long-term gains.

### Data and Identifiers
According to structured data from Wikidata and academic sources, the entity has specific properties that define its digital footprint:
- **Wikidata Description:** "sub-field of reinforcement learning"
- **Sitelink Count:** 6 (associated Wikipedia pages)
- **Languages:** Available in Catalan (ca), English (en), Spanish (es), Persian (fa), Hebrew (he), and Ukrainian (uk).