Reinforcement Learning and Deep RL Python Theory and Projects - Loading and Understanding the Environment

The video tutorial introduces the setup of a reinforcement learning environment using Stable Baselines 3. It covers importing necessary libraries, handling common import errors, and setting up a gym environment. The tutorial then demonstrates writing a generic workflow for reinforcement learning algorithms, focusing on random actions and score tracking. Finally, it explores the action and observation spaces, explaining their discrete and box formats, and prepares for future model training.

What new insight or understanding did you gain from this video?