For example in Cart Pole v1 gym environment the state space is continuous, but we discretize it to apply the Q-Learning algorithm because Q-Learning is a tabular method and only works with discrete state and action spaces.

In some examples of DQN use for solving Cart Pole gym environment, they use directly each state variable as input for the neural network (NN), therefore my doubt is: Can this input state variables for the NN be discretized in bins allowing to solve the cartpole environment ?

Thanks.