When you’re teaching an AI how to solve a maze using Q-learning, you might find that your intelligent agent excels in one maze but completely fails in another. This is a common issue, especially if you’re using a basic form of Q-learning known as tabular Q-learning. In this approach, we essentially create a table to store and update the “Q-values” for each possible state-action combination in the maze.
The problem arises because tabular Q-learning, in its simplest form, learns values that are incredibly specific. Imagine your maze as a grid. With tabular Q-learning, you are learning a unique Q-value for each cell in that grid and for every possible action (like moving up, down, left, or right) within that cell. These learned values are inherently tied to the exact layout of the maze you used for training.
This means that the AI is learning to solve that particular maze and no other. The fundamental reason is that the states it learns about – essentially positions within the maze grid – are unique to that specific maze. If you change the maze, even slightly, the states the AI has learned about simply don’t exist in the new maze. It’s as if you taught someone to navigate a specific room by memorizing every step, and then expected them to navigate a completely different room with the same instructions.
To make an AI truly learn how to solve a maze in a more general way, we need to move beyond this very specific state representation. Instead of defining a state by its coordinates in a grid, we should think about describing a state using more general features that are relevant regardless of the maze layout.
Consider these alternative ways to represent what the AI “sees” in the maze:
- Feature-Based States: Instead of just knowing “I am at grid coordinate (x, y)”, the AI could perceive its surroundings through features like:
- “Is there a wall directly in front of me?”
- “Is there a wall to my immediate right?”
- “Is there a wall to my immediate left?”
By using these types of features, the AI learns about relationships to its environment that are consistent across different mazes. A wall to the right is still a wall to the right, whether it’s in maze A or maze B.
- Pixel-Based Inputs: If your AI can “see” the maze, for example, through a top-down camera view or even a first-person perspective, you could use pixel data as input. This allows the AI to learn directly from visual information.
However, switching to feature-based or pixel-based states means we can’t use tabular Q-learning directly anymore. We need to employ function approximation techniques. These methods allow the AI to generalize from its experiences and estimate Q-values for states it hasn’t explicitly encountered before. Techniques like neural networks can be used to approximate the Q-function based on these more abstract state representations.
Finally, to truly enable your AI to learn how to solve mazes in general, and not just a single maze, you should train it on a variety of different mazes. This exposure to diverse environments is crucial for preventing overfitting to a single maze structure and encourages the AI to learn more robust and transferable strategies for maze navigation.
