Reinforcement Learning, Deep Learning, Artificial Intelligence, Data Science.
approximator, the training can be unstable. To address these shortcomings, we add new incentives or penalties to the objective function. In general, we need more patience in RL than in DL. Often, RL methods work with a narrow range of hyperparameters and require an extensive search to locate them.