I have read most of Sutton and Barto's introductory text on reinforcement learning. I thought I would try to apply some of the RL algorithms in the book to a previous assignment I had done on Sokoban, in which you are in a maze-like grid environment, trying to stack three snowballs into a snowman on a predefined location on the grid.
The basic algorithms (MC control, Q-learning, or Dyna-Q) seemed to all be based on solving whichever specific maze the agent was trained on. For example, the transition probabilities of going from coordinate (1,2) to (1,3) would be different for different mazes (since in one maze, we could have an obstacle at (1,3)). An agent that calculates its rewards based on one maze using these algorithms doesn't seem like it would know what to do given a totally different maze. It would have to retrain: 1) either take real life actions to relearn from scratch how to navigate a maze, or 2) be given the model of the maze, either exact or approximate (which seems infeasible in a real life setting) so that planning without taking actions is possible.
When I started learning RL, I thought that it would be more generalizable. This leads me to the question: Is this problem covered in multi-task RL? How would you categorize the various areas of RL in terms of the general problem that it is looking to solve?