2019年11月24日 · Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc.

The first comprehensive introduction to multi-agent reinforcement learning, an area of machine learning in which multiple decision-making agents learn to optimally interact in a shared environment. The book can be ordered online from stores (see MIT Press page ).

2024年5月29日 · What is Multi-Agent Reinforcement Learning (MARL)? Multi-Agent Reinforcement Learning (MARL) refers to the application of single-agent reinforcement learning in scenarios in which multiple agents can communicate …

This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning. We focus primarily on literature from recent years that combines deep reinforcement learning methods with a multi-agent scenario.

When we do multi-agents reinforcement learning (MARL), we are in a situation where we have multiple agents that share and interact in a common environment. For instance, you can think of a warehouse where multiple robots need to navigate to load and unload packages .

2023年12月15日 · This survey examines these challenges, placing an emphasis on studying seminal concepts from game theory (GT) and machine learning (ML) and connecting them to recent advancements in multi-agent reinforcement learning (MARL), i.e. the research of data-driven decision-making within MAS.

2024年6月1日 · Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper presents a comprehensive survey of MARL and its applications. We trace the historical evolution of MARL, highlight its progress, and discuss related survey works.

2 天之前 · Multi-agent reinforcement learning Multi-agent reinforcement learning (MARL) has shown significant promise in complex cooperative tasks , with advancements such as value decomposition aiding in credit assignment and intrinsic rewards [26, 27] …

2025年2月19日 · Multi-agent reinforcement learning (MARL) is a method that introduces reinforcement learning theories and algorithms into multi-agent systems.

In this tutorial, we will be able to train both formulations, and we will also discuss how parameter-sharing (the practice of sharing the network parameters across the agents) impacts each. This tutorial is structured as follows: First, we will define a set of hyperparameters we will be using.