6. Hyperparameter Tuning

Hyperparameter tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm, which includes reinforcement learning, evolutionary, and neuroevolution algorithms of NEORL. Hyperparameter tuning is effective to maximize the efficiency of the optimization algorithm in hand. In NEORL, we provide different methods to tune hyperparameters, which are highlighted briefly here.