I'm trying to use Q-learning in order to solve Wumpus world environment.
Wumpus world is a toy problem on 4x4 gridworld. The agent starts in entry position of the cave, looks for gold (agent can sense that he is on the gold field), then he has to pick it up and leave cave in the entry position. Some fields are safe, other contain pit or wumpus (monster). If agent move to the pit or wumpus field he dies. The fields next to wumpus or pit (not diagonally!) have properties that agent can sense - stench (wumpus), breeze (pit). Agent achieve positive reward if he leaves cave with gold and negative if he dies. Action space: turn right/left, move forward, shoot arrow (if shot in good direction it can kill wumpus, only 1 available), pick up gold, leave cave. There: https://www.javatpoint.com/the-wumpus-world-in-artificial-intelligence you can find more detailed description of environment.
It is easy to solve this problem if the gridworld is constant. I have a huge problem to even start thinking about it if gridworld is random in every learning episode (random fields and random size) and also random in testing. How should I define it (especially state space)? How should I create q table? I'm using Python.
Thank you in advance for any help.