Trading strategies are in constant transformation, especially today, given the growing use of quantitative and technological bases, taking advantage of the development of methods in other areas of knowledge such as mathematics, physics or statistics, which have set a precedent either by offering greater precision or by being more efficient methods to solve problems that originated in the framework of these other branches of knowledge. It is not in vain that it is important to implement models that, given the information available in the capital markets, allow investment decisions to be made, particularly in this case in trading, and at the same time generating the highest possible profitability. However, finding a model that incorporates the greatest amount of dynamics of reality with great precision is complex, despite the fact that many advances in quantitative modeling have allowed us to get closer to this objective, especially because these dynamics are not constant and are not deterministic. It is for this reason that, in response to the need to incorporate the evolution in the market dynamics itself through the available price information, some methods such as reinforced learning have become more important within the range of models studied and implemented in money desks. This is why this work develops the implementation of some reinforced learning algorithms on a selection of assets of the current S&P 500 indicator, which generate profitable positions in a pair trading strategy.