From Game-theoretic Multi-agent Log Linear Learning to Reinforcement Learning
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Multi-agent Systems (MASs) have found a variety of industrial applications from economics to robotics, owing to their high adaptability, scalability and applicability. However, with the increasing complexity of MASs, multi-agent control has become a challenging problem to solve. Among different approaches to deal with this complex problem, game theoretic learning recently has received researchers' attention as a possible solution. In such learning scheme, by playing a game, each agent eventually discovers a solution on its own. The main focus of this paper is on enhancement of two types of game-theoretic learning algorithms: log linear learning and reinforcement learning. Each algorithm proposed in this paper, relaxes and imposes different assumptions to fit a class of MAS problems. Numerical experiments are also conducted to verify each algorithm's robustness and performance.
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