In this talk, we discuss contextual bandits and reinforcement learning problems based on function approximation frameworks. For the first part, we consider neural logistic bandits, where the main task is to learn an unknown reward function within a logistic link function using a neural network. For the second part, we explain algorithms for learning Markov decision processes whose transition is governed by a multinomial logit model.