Write a script in Python that makes a model-based ai which can beat Space Invaders
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I'll do my best to help., I can give you some guidance on how to approach building a model-based AI for Space Invaders. However, creating a fully functional model-based AI is a complex and time-consuming task that requires extensive knowledge of both the game and the AI techniques. Therefore, the code snippet provided below serves only as an outline and a starting point for your project.

Here are the steps to build a model-based AI for Space Invaders:

Step 1: Pre-processing

To build a model-based AI, you will need to extract relevant information from the game screen, such as the positions of the player and the enemies, the bullets, and the obstacles. You can use computer vision techniques to accomplish this task.

Step 2: State representation

The next step is to represent the game state in a way that can be used as input to your AI model. You may want to consider representing the state as a vector of relevant features, such as the position of the player, the number of enemies remaining, and the player's score.

Step 3: Model selection

Next, you will need to choose an appropriate model for your AI. You may want to consider using reinforcement learning techniques, such as Q-learning or deep reinforcement learning, to train your model. You can also use pre-trained models, such as convolutional neural networks, to recognize game objects.

Step 4: Training

Once you have selected a model, you will need to train it on a large dataset of game states and actions. You can use techniques such as experience replay and target network to improve the stability of your model.

Step 5: Evaluation

Finally, you will need to evaluate your model's performance on a set of test games. You may want to consider using metrics such as the average score or the percentage of games won to evaluate your model's performance.

Here is some sample code to get you started:
```python

import numpy as np
import tensorflow as tf

# Pre-process the game screen to extract relevant information

# Represent the game state as a vector of relevant features

# Choose an appropriate model for your AI

# Train your model on a large dataset of game states and actions

# Evaluate your model's performance on a set of test games
```
Note that the code above is only a starting point, and you will need to modify and extend it based on your specific needs and the game requirements. Good luck!
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