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The main focus of this paper is on enhancement of two types of game-theoreticlearning algorithms: log-linear learning and reinforcement learning. Thestandard analysis of log-linear learning needs a highly structured environment,i.e. strong assumptions about the game from an implementation perspective. Inthis paper, we introduce a variant of log-linear learning that providesasymptotic guarantees while relaxing the structural assumptions to includesynchronous updates and limitations in information available to the players. Onthe other hand, model-free reinforcement learning is able to perform even underweaker assumptions on players' knowledge about the environment and otherplayers' strategies. We propose a reinforcement algorithm that uses adouble-aggregation scheme in order to deepen players' insight about theenvironment and constant learning step-size which achieves a higher convergencerate. Numerical experiments are conducted to verify each algorithm's robustnessand performance.

From Game-theoretic Multi-agent Log Linear Learning to Reinforcement Learning