In Q-learning, states need to be just X and Y positions of the agent, or a state can be several other characteristics?

Question

For example, in this article: https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/, which explains Q-learnig, teaches the Smartcab problem, the environment is a 5x5 grid. In this example, states are positions where the agent is.

In Q-learning, states need to be just X and Y positions to the agent moviments as in the grid like in the Smartcab example, or instead, depending on the problem a state can be several other characteristics such as speed, temperature, pression, quantity, and others type of characteristics ?

Answer 1

The states in your SmartCab example are not just the total number of 25 ($5 \times 5$) X and Y positions to the agent (taxi) movements which cannot completely describe any episode involving this gym environment and the agent, since the environment here also has a passenger associated with four specific locations to pickup (including one additional passenger state of being inside the taxi) and the same four specific locations as possibly correct destination to end any episode, both of whose X and Y positions the Q-learning algo needs to be aware of in order to proceed. Thus you have total of $5 \times 5 \times 5 \times 4=500$ states.

Of course depending on the problem, a state can have several other characteristics such as speed, acceleration, temperature, steering wheel angle and many other characteristics as obviously the case in real world autonomous driving RL applications.