  The main focus of this paper is on enhancement of two types of game-theoretic
learning algorithms: log-linear learning and reinforcement learning. The
standard analysis of log-linear learning needs a highly structured environment,
i.e. strong assumptions about the game from an implementation perspective. In
this paper, we introduce a variant of log-linear learning that provides
asymptotic guarantees while relaxing the structural assumptions to include
synchronous updates and limitations in information available to the players. On
the other hand, model-free reinforcement learning is able to perform even under
weaker assumptions on players' knowledge about the environment and other
players' strategies. We propose a reinforcement algorithm that uses a
double-aggregation scheme in order to deepen players' insight about the
environment and constant learning step-size which achieves a higher convergence
rate. Numerical experiments are conducted to verify each algorithm's robustness
and performance.
