Faster Learning in RL: Sum to Update Formula + Optimistic Initialization Explained

RL Secrets: Optimistic Initialization + Q-Value Update Formula | Try Try Tiger

Multi-Armed Bandits Explained | Greedy vs Epsilon-Greedy | Try Try Tiger’s Food Casino!

Explore vs Exploit Explained | Try Try Tiger’s Big Food Dilemma!

TRY TRY TIGER Enters Human World | Return & Discount Factor (Gamma) Simplified

Reward System in RL Explained | Rat Tail Story & Reward Hacking | Reinforcement Learning EP 3

RL Basics: Agent, Environment, State, Action, Reward, Policy

Introduction to the AI World — What Every Beginner MUST Know!