Deploying Model - Halite II 2017 Artificial Intelligence Competition p.7

TL;DR

This tutorial demonstrates how to use deep learning to create an AI bot for the game Halite, making use of the TensorFlow and Keras libraries.

Transcript

what's going on everybody and welcome to part 7 of the highlight 2 tutorial series as well as part 4 of the deep learning in halite series so in the tutorial leading up to this point we built a bot to randomly play against itself basically taking random choices and then we took the winner and save that to training data so we trained an AI to basica... Read More

Key Insights

• 😒 The tutorial demonstrates how to use deep learning techniques to create a more sophisticated AI bot for the game Halite.
• 📚 TensorFlow and Keras are powerful libraries for training and loading deep learning models.
• 🤖 The pick a new plan function is crucial for guiding the AI bot's decision-making process.

Questions & Answers

Q: What is the purpose of this tutorial?

This tutorial aims to teach how to use deep learning to create an AI bot for Halite, a game where the bot competes against other bots.

Q: What is the role of TensorFlow and Keras in this tutorial?

TensorFlow and Keras are used to train and load the deep learning model that guides the AI bot's decisions.

Q: What does the pick a new plan function do?

The pick a new plan function is responsible for selecting the next move for the AI bot based on the output of the deep learning classifier.

Q: How is the AI bot's model trained?

The model is trained by having the AI bot play against itself and saving the winning moves as training data.

Summary & Key Takeaways

• The tutorial takes a bot that was previously trained to play randomly and uses a deep learning classifier to guide its decisions.

• The AI bot is trained using TensorFlow and Keras, with the pick a new plan function being the crux of the AI.

• The tutorial covers importing the necessary libraries, loading the trained model, and making the AI bot play based on the deep learning classifier's output.