Table of Contents
1 Cover
4 Preface
7 1 Introduction 1.1 Introduction 1.2 Single Agent Planning 1.3 Multi‐agent Planning and Coordination 1.4 Coordination by Optimization Algorithm 1.5 Summary References
8 2 Improve Convergence Speed of Multi‐Agent Q‐Learning for Cooperative Task Planning 2.1 Introduction 2.2 Literature Review 2.3 Preliminaries 2.4 Proposed MAQL 2.5 Proposed FCMQL Algorithms and Their Convergence Analysis 2.6 FCMQL‐Based Cooperative Multi‐agent Planning 2.7 Experiments and Results 2.8 Conclusions 2.9 Summary 2.A More Details on Experimental Results References
9 3 Consensus Q‐Learning for Multi‐agent Cooperative Planning 3.1 Introduction 3.2 Preliminaries 3.3 Consensus 3.4 Proposed CoQL and Planning 3.5 Experiments and Results 3.6 Conclusions 3.7 Summary References
10 4 An Efficient Computing of Correlated Equilibrium for Cooperative Q‐Learning‐Based Multi‐Robot Planning 4.1 Introduction 4.2 Single‐Agent Q‐Learning and Equilibrium‐Based MAQL 4.3 Proposed Cooperative MAQL and Planning 4.4 Complexity Analysis 4.5 Simulation and Experimental Results 4.6 Conclusion 4.7 Summary Appendix 4.A Supporting Algorithm and Mathematical Analysis References
11 5 A Modified Imperialist Competitive Algorithm for Multi‐Robot Stick‐Carrying Application 5.1 Introduction 5.2 Problem Formulation for Multi‐Robot Stick‐Carrying 5.3 Proposed Hybrid Algorithm 5.4 An Overview of FA 5.5 Proposed ICFA 5.6 Simulation Results 5.7 Computer Simulation and Experiment 5.8 Conclusion 5.9 Summary Appendix 5.A Additional Comparison of ICFA References
12 6 Conclusions and Future Directions 6.1 Conclusions 6.2 Future Directions
13 Index
List of Tables
1 Chapter 1Table 1.1 Trace of Dijkstra's algorithm for Figure 1.11.Table 1.2 Trace of A* algorithm from Figure 1.10.Table 1.3 Trace of D* algorithm from Figure 1.12.Table 1.4 Expected reward of R1 and R2 at MSNE.
2 Chapter 2Table 2.1 List of acronyms.Table
