at least one processor comprising instructions executable by the at least one processor to:

receive data representing input to computer simulations by plural simulation players;

input the data to a training service of a first pipeline of model generation computerized services to train plural recommendation models;

use an inference service of the first pipeline to generate recommendations based on recommendation models trained using the training service in the first pipeline;

provide output of the inference service to an experimentation service of the first pipeline to test the recommendations to select a subset of the models using at least one key performance indicator (KPI);

use a training and an inference service of the second pipeline to provide recommendations of at least some of the recommendation models to train;

provide the recommendations of the at least some models to train generated by the second pipeline to the training service of the first pipeline;

execute a reinforcement learning model (RL) to use the training and inference services of the second pipeline to identify at least a first model from the first pipeline at least in part by maximizing a reward predicted for the first model, wherein the maximizing is executed at least in part by equating a recommendation associated with a time “t” to a reward associated with the time “t” plus a product of a discount factor and a recommendation associated with a time t+1; and

execute at least one of the recommendation models to provide recommendations for new computer simulations to provide to players of at least one computer simulation, the computer simulations comprising computer games.