《多主體強化學習協作策略研究(英文)》清晰地介紹了多主體、強化學習及多主體協作等基本概念和基礎內容，明確地闡述了有關多主體強化學習、協作策略研究的發展過程及最新動向，深入地探討了多主體強化學習與協作策略的理論與方法，具體地分析了多主體強化學習與協作策略在相關研究領域的套用方法。全書系統脈絡清晰、基本概念清楚、圖表分析直觀，注重內容的體系化和實用性。通過此書的閱讀和學習，讀者即可掌握多主體強化學習及協作策略的理論和方法，更可了解在實際工作中套用這些研究成果的手段。
基本介紹
- 中文名：多主體強化學習協作策略研究
- 外文名：Reinforcement Learning and Coordination in Multiagent Systems
- 作者：孫若瑩 趙剛
- 出版日期：2014年8月1日
- 語種：簡體中文, 英語
- ISBN：7302368309
- 出版社：清華大學出版社
- 頁數：164 頁
- 開本：16 開
內容簡介,圖書信息,圖書目錄,
內容簡介
《多主體強化學習協作策略研究(英文)》可作為從事計算機套用、人工智慧、自動控制、以及經濟管理等領域研究者的學習和閱讀參考，同時高等院校相關專業研究生以及人工智慧愛好者也可從中獲得借鑑。
圖書信息
多主體強化學習協作策略研究
作者：孫若瑩、趙剛
定價：48元
印次：1-1
ISBN：9787302368304
出版日期：2014.08.01
印刷日期：2014.07.23
出版社：清華大學出版社
圖書目錄
Chapter 1 Introduction
1.1 Reinforcement Learning
1.1.1 Generality of Reinforcement Learning
1.1.2 Reinforcement Learning on Markov Decision Processes
1.1.3 Integrating Reinforcement Learning into Agent Architecture
1.2 Multiagent Reinforcement Learning
1.2.1 Multiagent Systems
1.2.2 Reinforcement Learning in Multiagent Systems
1.2.3 Learning and Coordination in Multiagent Systems
1.3 Ant System for Stochastic Combinatorial Optimization
1.3.1 Ants Forage Behavior
1.3.2 Ant Colony Optimization
1.3.3 MAX-MIN Ant System
1.4 Motivations and Consequences
1.5 Book Summary
Bibliography
Chapter 2 Reinforcement Learning and Its Combination with Ant Colony System
2.1 Introduction
2.2 Investigation into Reinforcement Learning and Swarm Intelligence
2.2.1 Temporal Differences Learning Method
2.2.2 Active Exploration and Experience Replay in Reinforcement Learning
2.2.3 Ant Colony System for Traveling Salesman Problem
2.3 The Q-ACS Multiagent Learning Method
2.3.I The Q-ACS Learning Algorithm
2.3.2 Some Properties of the Q-ACS Learning Method
2.3.3 Relation with Ant-Q Learning Method
2.4 Simulat'ions and Results
2.5 Conclusions
Bibliography
Chapter 3 Multiagent Learning Methods Based on Indirect Media Information Sharing
3.1 Introduction
3.2 The Multiagent Learning Method Considering Statistics Features
3.2.I Accelerated K-certainty Exploration
3.2.2 The T-ACS Learning Algorithm
3.3 The Heterogeneous Agents Learning
3.3.1 The D-ACS Learning Algorithm
3.3.2 Some Discussions about the D-ACS Learning Algorithm
3.4 Comparisons with Related State-of-the-arts
3.5 Simulations and Results
3.5.1 Experimental Results on Hunter Game
3.5.2 Experimental Results on Traveling Salesman Problem
3.6 Conclusions
Bibliography
Chapter 4 Action Conversion Mechanism in Multiagent Reinforcement Learning
4.1 Introduction
4.2 Model-Based Reinforcement Learning
4.2.1 Dyna-Q Architecture
4.2.2 Prioritized Sweeping Method
4.2.3 Minimax Search and Reinforcement Learning
4.2.4 RTP-Q Learning
4.3 The Q-ac Multiagent Reinforcement Learning
4.3.1 Task Model
4.3.2 Converting Action
4.3.3 Multiagent Cooperation Methods
4.3.4 Q-value Update
4.3.5 The Q-ac Learning Algorithm
4.3.6 Using Adversarial Action Instead of s Probability Exploration
4.4 Simulations and Results
4.5 Conclusions
Bibliography
Chapter 5 Multiagent Learning Approaches Applied to Vehicle Routing Problems
Chapter 6 Multiagent learning Methods Applied to Multicast Routing Problems
Chapter 7 Multiagent Reinforcement Learning for Supply Chain Management
Chapter 8 Multiagent Learning Applied in Supply Chain Ordering Management
