Master the Game: Train a Neural Network with TensorFlow and Open AI

Master the Game: Train a Neural Network with TensorFlow and Open AI

Table of Contents:

1. Introduction
2. Building the Initial Population
3. Generating Training Samples
4. Analyzing the Game Results
5. Converting Training Data to numpy array
6. Printing Data Metrics
7. Conclusion

Introduction

Welcome to part two of our deep learning with games tutorial. In this tutorial, we will be building on the previous tutorial where we demonstrated how random games look. This time, we will define the initial population of data and generate training samples for our environment. We will also analyze the game results and convert the training data to a numpy array. Let's dive in!

Building the Initial Population

To start, we need to define our initial population of data. This is necessary for generating training samples in our environment. We will Create an empty list for the training data and scores. Additionally, we will have an accepted score training data to store the observations and moves made if the score is above 50. We will iterate through the plausible steps to choose a random action and store the game memory. The previous observation will be saved and updated as we progress through the game.

Generating Training Samples

In order to generate training samples, we will iterate through a certain number of gameplay steps. For each step, we will choose a random action and store the observation and action in the game memory. If there is a previous observation, we will append it to the game memory. The score will be updated Based on the reward received. If the game ends, we will check if the score meets the requirement for acceptance. If so, we will append the game memory data.

Analyzing the Game Results

Once we have played the games and collected the data, we will analyze the game results. We will check if the score is greater than or equal to the score requirement and append it to the accepted scores. We will then convert the game memory data to a one-hot output for training. Finally, we will save the training data.

Converting Training Data to numpy array

To prepare the data for training, we will convert the training data to a numpy array. This step allows us to easily manipulate and process the data. Once the data is converted, we will save it as a numpy file.

Printing Data Metrics

After the data is processed, we will print some data metrics. We will calculate the average accepted score and the median score. These metrics give us an idea of the performance of our model.

Conclusion

In this tutorial, we learned how to build the initial population of data, generate training samples, and analyze the game results. We also converted the training data to a numpy array and printed data metrics. In the next tutorial, we will create a neural network model to train on this data and test its performance in the game. Stay tuned!

Article: Deep Learning with Games: Building an Initial Population and Generating Training Samples

Welcome to part two of our deep learning with games tutorial series. In this tutorial, we will focus on building the initial population of data and generating training samples. These initial steps are crucial in setting up our environment for training our deep learning model to play games effectively.

To begin, we need to define our initial population of data. This involves creating an empty list for the training data and scores. The training data will store the observations and moves made during gameplay, while the scores will keep track of the scores achieved. We will also have an accepted score training data, where we will only append the training data if the score is above a certain threshold (in this case, 50).

Once we have defined our initial population, we can start generating training samples. To do this, we will iterate through a set number of gameplay steps. For each step, we will choose a random action to perform. This randomness allows our model to explore different possibilities and learn from its mistakes. We will store the observation and action taken in the game memory.

During gameplay, the score will be updated based on the rewards received. If the game reaches its end, we will check if the final score meets our acceptance criteria. If the score is above the threshold, we will append the game memory data to the accepted score training data. This ensures that we only train our model on gameplay instances where it performed well.

The next step is to analyze the game results. We will check if the score achieved is greater than or equal to our desired score requirement. If it is, we will convert the game memory data into a one-hot output for training. This conversion allows us to represent the different possible actions as distinct categories. We will then save this training data for later use.

Before we move on, let's take a moment to print some data metrics. We can calculate the average accepted score and the median score. These metrics give us insights into the performance of our model and the quality of the training data.

In conclusion, building the initial population and generating training samples are vital steps in our deep learning with games Journey. These steps lay the foundation for training our model effectively. In the next tutorial, we will create a neural network model to train on this data and test its performance in the game. Stay tuned for more exciting developments!

Highlights:

• Building the initial population of data is essential for generating training samples.
• We only append training data if the score meets our acceptance criteria.
• Analyzing game results helps us understand the performance of our model.
• Converting training data to a numpy array allows for easy manipulation and processing.
• Data metrics such as the average accepted score provide insights into model performance.

FAQ

Q: Why do we only append training data if the score is above a certain threshold? A: By only including instances where the model performed well, we ensure that it learns from successful gameplay strategies.

Q: How do we represent different actions in the training data? A: We convert the actions into a one-hot output format, where each action is represented as a distinct category.

Q: What do the data metrics, such as average accepted score, tell us? A: These metrics give us an idea of the overall performance of our model and the quality of the training data.