AI 101 - Course & Competition - Grades 7-12 - Sun@11:30am EST-Recording Workshop 8

Video Transcription

Video Summary

The video covers two main topics: text vectorization using the bag-of-words model with cosine similarity, and an introduction to reinforcement learning illustrated by the snake game.<br /><br />Initially, the instructor explains how to vectorize documents by compiling a vocabulary of unique words from two sample texts, counting word occurrences, and forming vectors. They then demonstrate calculating cosine similarity between two document vectors to measure similarity.<br /><br />The session then transitions to reinforcement learning, contrasting it with supervised and unsupervised learning. Reinforcement learning involves an agent learning to make decisions by receiving rewards from actions in an environment. The snake game serves as an example, where the agent (snake) learns to maximize rewards (eating apples) and avoid penalties (hitting walls or itself). States represent different configurations of the snake and apple; actions are movements, and rewards reflect outcomes of actions.<br /><br />The discussion includes defining state representations relative to the snake’s head and the apple’s position, handling many possible states, and the concept of rewards as negative or positive signals guiding learning. The necessity of a consistent coordinate system is emphasized.<br /><br />Finally, the concept of Q-learning is introduced—a reinforcement learning method that uses a Q-table to estimate expected cumulative rewards for state-action pairs. A stepwise example calculates Q-values and explains the role of the discount factor. The session ends with guidance for applying these concepts to tic-tac-toe and preparation for an upcoming competition, highlighting collaborative learning and resource use.

Keywords

text vectorization

bag-of-words model

cosine similarity

reinforcement learning

snake game

state representation

Q-learning

reward system

tic-tac-toe competition