Generate an advanced python script with pygame that envelops this idea:

```
The "Modular Back-Tracking Agent" (MBA) is a maze generation algorithm that integrates the efficiency of backtracking with modularity for flexibility and scalability. This approach will allow you to generate mazes with different modular components (like rooms or corridors), allowing the maze structure to evolve by splitting and merging pathways based on specific rules. Here's how you can implement this algorithm:

### 1. **Define the Modular Components**
In this algorithm, the maze will consist of various **modules**: these can be individual rooms, corridors, or sections of the maze that connect multiple points. Each module can have a predefined structure (like a 3x3 grid or specific configurations) or be dynamically generated.

### 2. **Modular Backtracking**
The backtracking agent will navigate through the maze, building connections between the modules. When the agent reaches a dead-end or completes a section, it will backtrack to find an alternative route or module to expand the maze.

### 3. **Algorithm Steps**

1. **Initialization**:
   - Define the grid size (e.g., `width` x `height`).
   - Define the maze modules (rooms, corridors, etc.), with specific sizes and connection rules.

2. **Starting Point**:
   - Select an initial position in the grid, and place the first module there (e.g., the starting room).

3. **Expand Maze with Modular Components**:
   - Choose a random direction to explore from the current module.
   - Check if the chosen direction connects to another module or if it is a valid direction to place a new module.
   - Place the new module in that direction, ensuring it connects properly to the existing structure (create passages between modules).
   - If a valid connection is made, mark the module as visited.

4. **Backtracking**:
   - If the agent encounters a dead-end or cannot place a new module, backtrack to the previous valid position where a new module can be placed.
   - Continue until the entire grid is filled or a stopping condition (like a max path length or max number of modules) is met.

5. **End Condition**:
   - Stop the maze generation process when all modules are placed, and all connections are made.

6. **Final Maze**:
   - The final maze consists of interconnected modules forming a unique maze structure.

### 4. **Pseudocode**
```python
class ModularBackTrackingAgent:
    def __init__(self, width, height, modules):
        self.width = width
        self.height = height
        self.modules = modules  # List of modules to use for the maze
        self.grid = [[' ' for _ in range(height)] for _ in range(width)]
        self.visited = set()
        self.path = []

    def place_module(self, x, y, module):
        # Check if module can be placed at (x, y)
        # If valid, place the module on the grid
        pass

    def get_random_direction(self, x, y):
        # Randomly choose a direction to explore (up, down, left, right)
        pass

    def backtrack(self):
        # Backtrack to the last valid position to explore other directions
        pass

    def generate(self, start_x, start_y):
        # Start maze generation from (start_x, start_y)
        self.place_module(start_x, start_y, self.modules[0])
        self.visited.add((start_x, start_y))
        self.path.append((start_x, start_y))

        while self.path:
            x, y = self.path[-1]
            direction = self.get_random_direction(x, y)

            if direction:  # If a valid direction is found
                self.place_module(x, y, direction)
                self.visited.add(direction)
                self.path.append(direction)
            else:  # No valid direction, need to backtrack
                self.backtrack()

        return self.grid
```

### 5. **Key Features and Customization**

- **Modular Design**: Different maze modules can be used to create complex structures (e.g., rooms, corridors, open spaces).
- **Backtracking**: The backtracking agent ensures that the maze can be fully connected while providing randomness in the paths.
- **Scalability**: You can easily modify the algorithm to introduce different types of modules and maze layouts.
- **Flexibility**: The modular components can be dynamically generated or predefined, allowing for infinite variations of maze designs.

### 6. **Example Modules**

1. **Room Module**: A square grid (3x3) representing a room.
2. **Corridor Module**: A linear path of a specified length.
3. **Dead-End Module**: A module that leads to a dead-end, forcing backtracking.

### 7. **Implementation Ideas**
- To make the maze more complex, you can introduce additional rules, like generating larger or smaller modules based on certain conditions (e.g., room sizes increase as the agent explores further).
- Introduce features like traps, key items, or enemies into the modules to create a more interactive maze.

This Modular Back-Tracking Agent (MBA) algorithm can be customized further, making it an excellent tool for generating adaptable, scalable mazes for games or AI testing.