Multi-Agent Coordination. Amit Konar

      1  Cover

      2  Title Page

      3  Copyright Page

      4  Preface

      5  Acknowledgments

      6  About the Authors

      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

      14  End User License Agreement

      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 2.2 Details of 10 × 10 grid maps.Table 2.3 Run‐time complexity of Algorithm 2.3 over reference algorithms in d...Table 2.4 Run‐time complexity of Algorithm 2.3 over reference algorithms in s...Table 2.5 Time taken by Khepera‐II mobile robots to reach a team‐goal with diffe...Table 2.A.1 Number of joint state–action pair converged in deterministic situ...Table 2.A.2 Number of joint state–action pair converged in stochastic situati...Table