Master Study AI

Multi-Agent Reinforcement Learning (MARL): Collaboration, Competition, and Coordination

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Table of Contents

Course Modules:

Module 1: Introduction to Multi-Agent Systems

What is Multi-Agent Reinforcement Learning?

Key challenges: non-stationarity, scalability, communication

Use cases in games, robotics, and social simulations

Module 2: Types of Agent Interactions

Cooperative vs. competitive vs. mixed environments

Communication protocols and shared goals

Emergent behavior and self-play dynamics

Module 3: Core MARL Algorithms

Independent Q-Learning

Joint Action Learners

Centralized Training with Decentralized Execution (CTDE)

 Module 4: Implementing Multi-Agent Environments

OpenAI Gym + PettingZoo and Multi-Agent Particle Environments

Defining multiple agents, action spaces, and rewards

Monitoring individual vs. collective learning

Module 5: Policy Sharing, Coordination & Self-Play

Parameter sharing strategies

Multi-agent actor-critic variants (e.g., MADDPG, QMIX)

Using self-play to train robust agents

Module 6: Capstone Project – Build a Multi-Agent System

Choose a multi-agent environment (e.g., Predator-Prey, Traffic Control)

Train cooperative or competitive agents

Submit training logs, strategy analysis, and performance plots

Tools & Technologies Used:

Python

PettingZoo, OpenAI Gym

PyTorch or TensorFlow (for deep MARL agents)

Seaborn, Matplotlib (for visualization)

Target Audience:

Advanced AI and reinforcement learning students

Researchers in robotics, simulation, or autonomous systems

Developers building intelligent multi-agent applications

Game designers and technical AI practitioners

Global Learning Benefits:

Understand complex agent interactions in shared environments

Build scalable, intelligent agent ecosystems

Apply MARL to real-world domains: logistics, finance, robotics

Gain hands-on experience with leading MARL frameworks

 

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