Game Theoretic Learning and Distributed Optimization in Memoryless Multi Agent Systems This book presents new efficient methods for optimization in realistic large scale multi agent systems. These methods do not require the agents to have the full information about the system but instead allow them to make their local decisions based only on the local information possibly obtained during scommunication with their local neighbors. The book primarily aimed at researchers in optimization and control considers three different information settings in multi agent systems: oracle based communication based and payoff based. For each of these information types an efficient optimization algorithm is developed which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions complicated communication topologies closed form expressions for costs and utilities and finiteness of the system’s state space.