Kimbrough, Steven. OLu, Ming2023-05-232023-05-232005-03-012016-07-25https://repository.upenn.edu/handle/20.500.14332/42284Repeated play in games by simple adaptive agents is investigated. The agents use Q-learning, a special form of reinforcement learning, to direct learning of behavioral strategies in a number of 2×2 games. The agents are able effectively to maximize the total wealth extracted. This often leads to Pareto optimal outcomes. When the rewards signals are sufficiently clear, Pareto optimal outcomes will largely be achieved. The effect can select Pareto outcomes that are not Nash equilibria and it can select Pareto optimal outcomes among Nash equilibria.The final publication is available at Springer via http://dx.doi.org/10.1007/s10257-003-0024-0Q-learningalgorithmic game theorygameslearning and gamesOther Social and Behavioral SciencesSet TheoryTheory and AlgorithmsArticle