Intuition behind predicting probability in reinforcement learning I'm learning how to implement reinforcement learning to solve classification problem. I understand this is not a common approach and might not return better performance compared to other supervised classifiers. In Sklearn, every model has its own calculation of predict_proba() to predict the probability of each label.
Does anyone have any thoughts on how to predict the probability of each label using reinforcement learning specifically using DQN (deep Q-network)?

No neural network produces binary classifications. Neural networks are defined in terms of functions of floating-point numbers and the final layer of neural network also returns floating-point outputs. The outputs can be used to make hard classifications by thresholding them. Those outputs are either unbounded, or are passed through some kind of activation function (ReLU, tanh, sigmoid, softmax, etc). If you need probabilities of binary events you use sigmoid, if you have multi-class data, you use softmax, so the returned values can be interpreted as probabilities.
