商品描述
The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL's models, solution concepts, algorithmic ideas, technical challenges, and modern approaches. Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the field's foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers.
- First textbook to introduce the foundations and applications of MARL, written by experts in the field
- Integrates reinforcement learning, deep learning, and game theory
- Practical focus covers considerations for running experiments and describes environments for testing MARL algorithms
- Explains complex concepts in clear and simple language
- Classroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics
- Resources include code and slides
商品描述(中文翻譯)
《多智能體強化學習（MARL）的首部全面介紹，涵蓋了MARL的模型、解決概念、算法思想、技術挑戰和現代方法。》
多智能體強化學習（MARL）是機器學習的一個領域，其中一群智能體學習在共享環境中進行最佳互動，並在現代生活中擁有越來越多的應用，從自動駕駛和多機器人工廠到自動交易和能源網絡管理。本書提供了對MARL中模型、解決概念、算法思想、技術挑戰和現代方法的清晰而嚴謹的介紹。書中首先介紹了該領域的基礎，包括強化學習理論和算法的基本知識、互動遊戲模型、遊戲的不同解決概念，以及支撐MARL研究的算法思想。接著詳細介紹了當代MARL算法，這些算法利用深度學習技術，涵蓋了集中訓練與分散執行、價值分解、參數共享和自我對弈等概念。本書附帶了自己的MARL代碼庫，使用Python編寫，包含自包含且易於閱讀的MARL算法實現。技術內容以易於理解的語言解釋，並用大量示例進行說明，為新手闡明MARL，同時為更高級的讀者提供高層次的見解。
- 首部介紹MARL基礎和應用的教科書，由該領域的專家撰寫
- 整合強化學習、深度學習和博弈論
- 實用焦點涵蓋實驗運行的考量，並描述測試MARL算法的環境
- 以清晰簡單的語言解釋複雜概念
- 經過課堂測試，適合計算機科學、人工智慧和機器人領域的研究生和專業人士
- 資源包括代碼和幻燈片
作者簡介
Stefano V. Albrecht is Associate Professor in the School of Informatics at the University of Edinburgh, where he leads the Autonomous Agents Research Group. His research focuses on the development of machine learning algorithms for autonomous systems control and decision making, with a particular focus on deep reinforcement learning and multi-agent interaction.
Filippos Christianos is a research scientist in multi-agent deep reinforcement learning focusing on how MARL algorithms can be used efficiently and the author of multiple popular MARL-focused code libraries.
Lukas Schäfer is a researcher focusing on the development of more generalizable, robust, and sample-efficient decision making using deep reinforcement learning, with a particular focus on multi-agent reinforcement learning.
作者簡介(中文翻譯)
Stefano V. Albrecht 是愛丁堡大學資訊學院的副教授，負責自主代理研究小組。他的研究專注於自主系統控制和決策的機器學習演算法開發，特別關注深度強化學習和多代理互動。
Filippos Christianos 是一位研究科學家，專注於多代理深度強化學習，研究如何有效地使用 MARL 演算法，並且是多個受歡迎的 MARL 專注代碼庫的作者。
Lukas Schäfer 是一位研究人員，專注於使用深度強化學習開發更具通用性、穩健性和樣本效率的決策，特別關注多代理強化學習。
