AI Game Bot for Teamfight Tactics (TFT)

In Progress

Technologies Used: Python, Machine Learning, TensorFlow, Neural Networks, Reinforcement Learning

Description:
This project involves developing AI bots that analyze Teamfight Tactics (TFT) game data using machine learning algorithms. The bots process historical game data, predict optimal strategies, and learn from past matches to enhance their decision-making over time.

Key Features:

• Game Data Analysis:
  The AI processes game states, including champion placements, item builds, and opponent strategies, to identify key patterns and inform future strategy decisions.

• Neural Networks:
  Uses deep learning techniques, particularly neural networks, to train the bots on historical data, optimizing the prediction of successful strategies.

• Reinforcement Learning:
  Applies reinforcement learning algorithms to refine strategies by rewarding effective decision-making and penalizing suboptimal choices.

• Strategy Optimization:
  The bots analyze past gameplay to suggest optimal compositions and positioning strategies based on the data they have processed, adapting to the evolving meta.

• Performance Metrics:
  Tracks performance by evaluating win rates, rankings, and the effectiveness of strategies used in past matches, providing insights for future improvements.

Why It’s Special:
This project presented a unique challenge where I could explore the intersection of AI, machine learning, and game strategy. By creating an AI that didn’t play the game itself but analyzed and optimized strategies based on past data, I was able to gain valuable insights into reinforcement learning and neural network techniques. The iterative process of refining strategies based on data has provided me with hands-on experience in game AI development and deepened my passion for applying AI to real-world problems, particularly in dynamic and competitive environments like TFT.