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This chapter concludes the book. The book identifies a few fundamental problems in multi‐robot coordination and proposes solutions to handle these problems by extending the traditional evolutionary algorithm (EA) and multi‐agent Q‐learning. It provides the preliminaries of Reinforcement Learning and EA in view of the multirobot coordination. The book proposes useful characteristic properties for exploration of the team‐goal and joint action selection in multi‐agent system. It also proposes a novel Consensus Q‐learning algorithm for multirobot cooperative planning. The book introduces a novel approach to correlated Q‐learning and subsequent multi‐robot planning. It also introduces a novel approach for efficiently employing both Imperialist Competitive Algorithm and Firefly Algorithm to develop a hybrid algorithm with an aim to utilize the composite benefits of the explorative and exploitative capabilities of both ancestor algorithms.

This chapter proposes consensus‐based multi‐agent Q‐learning (MAQL) to address the bottleneck of the optimal equilibrium selection among multiple types. It briefly introduces the adaption mechanism of single agent QL and the state‐of‐the‐art equilibrium‐based MAQL algorithms. Then the cooperative control problem employing PGs mainly focusing upon the consensus problem is briefly discussed. The chapter also proposes a novel consensus QL (CoQL). Subsequently, a consensus‐based multirobot cooperative planning algorithm is proposed. Then two experiments are presented. The first experiment is designed to study the relative performance of the CoQL over the reference algorithms. Another experiment is framed to study the relative performance of the consensus‐based planning algorithm over the reference algorithms, considering multi‐robot stick carrying problem as a benchmark in terms of state‐transitions required to complete the task.