Reinforcement Learning and Deep RL Python Theory and Projects - Visualizing the Training

The video tutorial covers the implementation of a code that improves over 181 episodes, focusing on moving averages. It demonstrates an agent playing a game live, balancing a pole by moving left or right. The tutorial explains how the agent improves its balancing over time with more training epochs. It also discusses rendering the screen in human mode and concludes with a brief overview of the session.

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