Joint-Action learner, in the context of repeated two-agent cooperative games. It shows that both types of learners achieve success in simple cooperative games. Lauer and Riedmiller [@lauer2000algorithm] propose the distributed Q-learning algorithm base on the optimistic assumption. It is proved that optimal joint actions can be guaranteed to achieve if the cooperative game is deterministic. FMQ heuristic [@kapetanakis2002reinforcement] and recursive FMQ [@matignon2008study] are proposed to alter the Q-value estimation function to handle the stochasticity of the games. FMQ obtains success in partially stochastic climbing games but still can not achieve satisfactory performance in fully stochastic cooperative games. Panait et al. [@panait2006lenient] propose the lenient multiagent learning algorithm to overcome the noise introduced by the stochasticity of the games. The results show that coordination on the optimal joint action can be achieved in more than 93.5% of the runs in the fully stochastic climbing game. Matignon et al. review all existing independent multiagent reinforcement learning algorithms and evaluate and discuss their strength and weakness. It shows that all of them fail to achieve perfect coordination for fully stochastic games and only recursive FMQ can achieve coordination for 58% of the runs.

The other line of research considers the multiagent coordination problem 