Lecture Notes: Training AI to Play Snake Using Reinforcement Learning

Introduction

• Instructor: Patrick Lober, popular Python instructor.
• Project: Build an AI to teach itself how to play the Snake game.
• Tools Used:
  • Pygame for game development.
  • PyTorch for deep learning.
• Learning Topic: Basics of Reinforcement Learning.

Final Project Demonstration

1. Starting the Script: Run python agents.py to begin training the agent.
2. Game Environment: A visual representation of the game with scores displayed.
3. Training Process:
   • The snake starts with no knowledge and makes random moves.
   • Improvement is gradual; expect around 80 to 100 games for a solid strategy (approximately 10 minutes).
   • Initial performance is poor but improves over time (demonstration of learning).

Course Structure

• Part 1: Theory of Reinforcement Learning.
• Part 2: Implementing the Snake game using Pygame.
• Part 3: Creating the AI agent.
• Part 4: Implementing the model using PyTorch.

Theory of Reinforcement Learning

• Definition: Reinforcement Learning (RL) involves teaching software agents to take actions in an environment to maximize cumulative rewards.
• Main Components:
  • Agent: The player (AI).
  • Environment: The game (Snake).
  • Rewards: Feedback that informs the agent of its performance (e.g., +10 for eating food, -10 for dying).
• Approaches: Various strategies; this course uses Deep Q-Learning, which implements a deep neural network to predict actions.

Code Overview

1. Game Implementation:
   • Create game loop that handles actions and updates the game state.
   • Calculate rewards and check for game over conditions.
2. Agent:
   • Integrates with the environment and contains the training loop.
3. Model:
   • A feedforward neural network for action prediction.

Important Variables and Functions

• Rewards:
  • +10 when food is eaten,
  • -10 on game over,
  • 0 for neutral actions.
• Actions: Represented as arrays to avoid immediate 180-degree turns.
• States: The agent needs to know about dangers around (e.g., boundaries, food position, direction).

Model and Training

• Deep Q-Learning: Update Q-values based on rewards and maximum future rewards (Bellman Equation).
• Training Functions:
  • train_short_memory: Updates based on recent actions.
  • train_long_memory: Uses past actions to enhance learning.

Setting Up the Environment

• Use Conda to manage dependencies for the project.
• Install required packages: Pygame, PyTorch, Matplotlib, IPython.

Conclusion

• The AI improves over time through iterative training.
• Final model can be saved for future use.
• Homework: Improve AI performance and handle edge cases better (e.g., preventing the snake from trapping itself).
• Encouragement to engage with the content and contribute feedback.