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Multi-Agent Reinforcement Learning
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Multi-Agent Reinforcement Learning

Foundations and Modern Approaches

Lukas Schäfer, Filippos Christianos and Stefano Albrecht
MIT Press, 2024
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Imagine a warehouse full of diverse goods, and AI-fueled agents tasked with picking them up and delivering them to a predetermined destination. Multi-agent reinforcement learning (MARL) makes this and other more complex scenarios viable. Emerging from reinforcement learning (RL), MARL enables multiple AI agents to learn approaches and undertake behaviors that optimize success in complicated, changing environments. Lukas Schäfer, Filippos Christianos, and Stefano Albrecht provide an illuminating account of MARL’s foundation, along with numerous examples. 

Summary

In a multi-agent system, multiple agents engage with an environment to achieve goals.

Consider a context in which a collection of autonomous, AI-driven agents share the same space. They are all capable of formulating plans, adopting “policies” about how they interact with their environment and the other agents in it, making decisions, and acting. They may have a collective goal, such as, for example, clearing a warehouse, and they may have individual goals, such as maximizing the return on risky investments. They learn how best to achieve their goals by assessing their environments, coordinating with each other, and ultimately, by trial and error.

Autonomous AI agents learn how to formulate effective policies and achieve their goals in potentially challenging environments through multi-agent reinforcement learning (MARL). MARL emerges from reinforcement learning, where agents attempt and sometimes fail to achieve their goals through actions, and receive or lose benefits based on whether their actions are successful.

Reinforcement learning for a single agent focuses on single agents developing plans and performing actions that maximize the realization...

About the Authors

Lukas Schäfer is an AI researcher at Microsoft Research with the goal of creating autonomous agents that can efficiently learn to solve complex decision-making tasks in the real world. Filippos Christianos is a research scientist specializing in Large Language Models (LLMs) and Reinforcement Learning (RL). Stefano Albrecht’s research is in the areas of autonomous agents, multi-agent interaction, reinforcement learning, and game theory, with a focus on sequential decision-making under uncertainty.

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