Unbelievable: AI masters Fall Guys with an undetectable bot

Unbelievable: AI masters Fall Guys with an undetectable bot

Table of Contents:

1. Introduction
2. Exploiting Fall Guys with Artificial Intelligence 2.1. Avoiding Fall Guys' Anti-cheating System 2.2. Collecting Training Data 2.3. Preprocessing the Data 2.4. Training the Neural Network
3. Using Google Colab to Train the Network 3.1. Setting Up Google Colab 3.2. Upgrading the Required Libraries 3.3. Loading and Preparing the Data 3.4. Training the Neural Network
4. Comparing Agent Performance 4.1. Testing a Random Agent 4.2. Evaluating the Trained Agent
5. Conclusion
6. FAQ

Exploiting Fall Guys with Artificial Intelligence

Fall Guys is a popular and addictive game that has gained a massive following. In this article, we will explore how You can use artificial intelligence (AI) to exploit the game and achieve incredible results. We will dive into the intricacies of Python programming and the fast.ai library, as well as the methods used by players to circumvent anti-cheating engines.

Avoiding Fall Guys' Anti-cheating System

Fall Guys has a robust anti-cheating system in place to prevent players from gaining an unfair AdVantage. To avoid triggering this system, we need to Create an AI agent that behaves in a human-like manner. This means that we cannot directly exploit the internal workings of the game. Instead, we will train our agent using my own gameplay data, making it play identically to a human player. However, we will erase the memories of mistakes or failed qualifications to ensure that our AI performs better than even the most skilled human players.

Collecting Training Data

To train our AI agent, we need to acquire a sufficient amount of training data. We achieve this by running a Python script in the background that captures screenshots of the game screen and records the keystrokes used during gameplay. Our agent will constantly move forward and decide between moving left, moving right, or jumping. We will focus on training our agent on three specific races: Door Dash, Gate Crash, and Hit Parade. The captured data includes the game screenshots and the associated keystrokes, providing us with the necessary information to train our neural network.

Preprocessing the Data

The captured game screenshots provide a rich source of data for training our neural network. However, the images are too complex for the neural network to process effectively. Therefore, we need to preprocess the images by simplifying them. We remove unnecessary color information, leaving only the essential elements for decision-making. We also simplify the textures and focus on key features such as edges and outlines. The resulting Simplified images provide enough information for our neural network to make informed decisions.

Training the Neural Network

With the preprocessed data in HAND, we can now train our neural network. We utilize the fast.ai library, which offers powerful tools and methods for training neural networks efficiently. By using a pre-trained ResNet-18 network as a starting point, we can leverage its learned features to classify our simplified game images effectively. Through a process of fine-tuning, we train the later layers of the network while leaving the earlier layers, which capture basic features, untouched. This approach allows us to create a functioning neural network with minimal amounts of training data.

Using Google Colab to Train the Network

Training a neural network can be computationally demanding, especially with large datasets. However, using Google Colab, we can significantly speed up the process by utilizing shared GPUs. Google Colab provides a free and accessible platform for training our neural network. By taking advantage of the shared GPU runtime, we can perform calculations much faster than on a regular CPU.

Setting Up Google Colab

To use Google Colab, we need to set it up initially. In the top right corner of this article, there is a video guide on how to set up Google Colab for free. Simply follow the instructions to get started.

Upgrading the Required Libraries

Once we have Google Colab set up, we need to upgrade the required libraries. This ensures that our fast.ai library functions correctly. Using the Python pandas library, we upgrade the necessary dependencies for fast.ai.

Loading and Preparing the Data

To train our neural network, we load the preprocessed data onto Google Colab from our Google Drive. This is achieved by mounting our Google Drive and accessing the saved data. We then load the data into a data loader using fast.ai. The images are labeled Based on the parent directory's name, which indicates the desired action. For example, the "jump" folder contains images where the agent should jump. We print out the data loader and a sample batch to ensure that the data loading process is successful.

Training the Neural Network

With the data loader set up, we proceed to train our neural network. We use a ResNet-18 network architecture, as it has proven to be effective for image classification. The fast.ai library provides the tools to train the network efficiently. We start by finding an appropriate learning rate using the learning rate finder. This helps us choose a learning rate that allows for optimal convergence. We then initiate the fine-tuning process, where we train the later layers of the network while keeping the earlier layers unchanged. This approach leverages the pre-trained features of the network while adapting it to our specific task.

Comparing Agent Performance

To evaluate the performance of our AI agent, we compare it against a randomly behaving agent. This baseline comparison allows us to gauge the effectiveness of our AI and determine if it is an improvement over completely random decision-making.

Testing a Random Agent

To establish our baseline, we run simulations with a completely random agent. This agent will always move forward but randomly choose between moving left, moving right, or jumping. By testing this random agent multiple times, we can determine the likelihood of it qualifying in any given round. The results confirm that a random agent is highly unlikely to qualify in Fall Guys consistently.

Evaluating the Trained Agent

We evaluate the performance of our trained AI agent by observing its gameplay in Fall Guys. The agent makes decisions based on the learned features from the training data, aiming to qualify in each round. Though our trained agent has only been exposed to a limited number of games, we can already observe its ability to correct itself and maximize its chances of qualifying. With further training on a larger dataset, we can expect even more impressive performance from our AI agent.

Conclusion

In this article, we have explored the exciting world of exploiting Fall Guys using artificial intelligence. By training a neural network on gameplay data, we can create an AI agent that performs at a level comparable to, or even surpassing, human players. We have discussed the process of avoiding Fall Guys' anti-cheating system, collecting and preprocessing training data, training the neural network, and evaluating agent performance. With further advancements in AI and machine learning, we can expect even more impressive feats in the world of gaming.

FAQ

Q: Can I use this AI agent to cheat in Fall Guys? A: While this AI agent exploits the game mechanics, it is essential to note that cheating in any form is against the game's rules. This article focuses on the application of AI in gaming and highlights the complexity of creating such agents. It is encouraged to play games within the rules and ethics set by the developers.

Q: How accurate is the AI agent in qualifying for rounds in Fall Guys? A: The accuracy of the AI agent depends on several factors, including the amount and quality of training data, the complexity of the game environment, and the training methodology. With sufficient training and fine-tuning, the AI agent can achieve a high qualification rate.

Q: Is it legal to use AI agents in gaming competitions? A: The use of AI agents in gaming competitions is subject to the rules and regulations set by the event organizers. It is essential to abide by these rules to ensure fair competition and maintain the integrity of the event.

Q: Can I train the AI agent on different races in Fall Guys? A: Yes, the AI agent can be trained on different races in Fall Guys by expanding the training dataset to include more varied gameplay scenarios. This will improve the agent's adaptability and performance in different game situations.