Multi-Agent Coordination Av Arup Kumar Sadhu, Amit Konar

<p><b>Discover the latest developments in multi-robot coordination techniques with this insightful and original resource</b><p><i>Multi-Agent Coordination: A Reinforcement Learning Approach</i> delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. <p>You''ll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting