I trained a language model from scratch to play snake

I trained a language model from scratch to play snake

---

---

AI Summary

Summary of Video: Learning to Play Snake Using Behavioral Cloning

1. Introduction to the Project
   • Focus on a language model that learns to play the Snake game using behavioral cloning, avoiding reinforcement learning.
   • Training shown in progress (Apoch 11), demonstrating effective gameplay.
2. Behavioral Cloning Approach
   • An expert algorithm is created based on breadth-first search for pathfinding to food.
   • Data generation process records gameplay of the expert algorithm.
3. Data Generation
   • generate_data.py script: plays 1000 games, saves data to expert_snake_data.pickle.
4. Model Training
   • A transformer encoder model is trained on recorded gameplay data to predict actions based on game state.
   • Key files involved:
     • train.py: Runs the training on the generated data.
     • snake_game.py: Core game logic, does not render during data generation.
     • expert_snake_player.py: Implements the expert algorithm with bounds checking and pathfinding.
5. Model Configuration
   • Details on hyperparameters:
     • Uses 4 action classes (up, down, left, right).
     • 8 attention heads, 6 transformer encoder layers, with dropout for overfitting mitigation.
   • Vocabulary specifies grid dimensions and cell states (empty, head, body, food, padding).
6. Training Metrics
   • Training progress shown through loss and accuracy metrics; aim for decreasing loss and increasing accuracy.
   • Fast learning recognized, with initial skepticism about the behavioral cloning approach.
7. Additional Resources
   • Options to download the project from Patreon, with modifiers available.
   • Encouraged users to check out the Patreon for further teaching resources and membership.