The problem of state representation in Reinforcement Learning (RL) is similar to problems of feature representation, feature selection and feature engineering in supervised or unsupervised learning.
Literature that teaches the basics of RL tends to use very simple environments so that all states can be enumerated. This simplifies value estimates into basic rolling averages in a table, which are easier to understand and implement. Tabular learning algorithms also have reasonable theoretical guarantees of convergence, which means if you can simplify your problem so that it has, say, less than a few million states, then this is worth trying.
Most interesting control problems will not fit into that number of states, even if you discretise them. This is due to the "curse of dimensionality". For those problems, you will typically represent your state as a vector of different features - e.g. for a robot, various positions, angles, velocities of mechanical parts. As with supervised learning, you may want to treat these for use with a specific learning process. For instance, typically you will want them all to be numeric, and if you want to use a neural network you should also normalise them to a standard range (e.g. -1 to 1).
In addition to the above concerns which apply for other machine learning, for RL, you also need to be concerned with the Markov Property - that the state provides enough information, so that you can accurately predict expected next rewards and next states given an action, without the need for any additional information. This does not need to be perfect, small differences due to e.g. variations in air density or temperature for a wheeled robot will not usually have a large impact on its navigation, and can be ignored. Any factor which is essentially random can also be ignored whilst sticking to RL theory - it may make the agent less optimal overall, but the theory will still work.
If there are consistent unknown factors that influence result, and could logically be deduced - maybe from history of state or actions - but you have excluded them from the state representation, then you may have a more serious problem, and the agent may fail to learn.
It is worth noting the difference here between observation and state. An observation is some data that you can collect. E.g. you may have sensors on your robot that feed back the positions of its joints. Because the state should possess the Markov Property, a single raw observation might not be enough data to make a suitable state. If that is the case, you can either apply your domain knowledge in order to construct a better state from available data, or you can try to use techniques designed for partially observable MDPs (POMDPs) - these effectively try to build missing parts of state data statistically. You could use a RNN or hidden markov model (also called a "belief state") for this, and in some way this is using a "learning or classification algorithms to "learn" those states" as you asked.
Finally, you need to consider the type of approximation model you want to use. A similar approach applies here as for supervised learning:
A simple linear regression with features engineered based on domain knowledge can do very well. You may need to work hard on trying different state representations so that the linear approximation works. The advantage is that this simpler approach is more robust against stability issues than non-linear approximation
A more complex non-linear function approximator, such as a multi-layer neural network. You can feed in a more "raw" state vector and hope that the hidden layers will find some structure or representation that leads to good estimates. In some ways, this too is "learning or classification algorithms to "learn" those states" , but in a different way to a RNN or HMM. This might be a sensible approach if your state was expressed naturally as a screen image - figuring out the feature engineering for image data by hand is very hard.
The Atari DQN work by DeepMind team used a combination of feature engineering and relying on deep neural network to achieve its results. The feature engineering included downsampling the image, reducing it to grey-scale and - importantly for the Markov Property - using four consecutive frames to represent a single state, so that information about velocity of objects was present in the state representation. The DNN then processed the images into higher-level features that could be used to make predictions about state values.