I am trying to learn machine learning from Andrew NG's Machine learning specialization on Coursera. In the chapter about reinforcement learning Andrew NG said that if you do not select correct hyperparameters your model can take a long time to train.
Are there any guidelines on picking hyperparameters for Deep Reinforcement Learning?
Let's say I have an agent who has 10000 states, how many layers and units my neural network should have? If I am using mini-batches how big should each batch be? If the states are contiguous states, how far back the agent should look before taking a decision?