Reinforcement Learning and Deep RL Python Theory and Projects - Introduction

This video module introduces key terminologies in reinforcement learning, emphasizing their importance for understanding the subject. Although the module is theoretical and may seem dry, it is essential for grasping future concepts. Key terms such as Environment, State, Agent, Action, Goal, Reward, Policy, and Episode are mentioned, with a promise of detailed explanations in upcoming videos. The module sets the stage for practical Python exercises that will follow.

Why is it important to learn the terminologies before diving into practical implementations?

Describe what an episode is in the context of reinforcement learning.