Exploring Starts And Epsilon Greedy Policies

# Exploring Starts And Epsilon-Greedy Policies: Mastering Monte Carlo Prediction Imagine training a self-driving car. It can't just learn from a textbook; it needs to explore different driving scenarios to understand which actions lead to the best outcomes. In Reinforcement Learning (RL), particularly in Model-Free Prediction using Monte Carlo methods, the way an agent *explores* the environment is crucial. Two fundamental techniques for this are **Exploring Starts** and **Epsilon-Greedy Polici