多主體強(qiáng)化學(xué)習(xí)協(xié)作策略研究

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
……
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