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 feature of the code is highlighted after 181 episodes?

• The code crashes frequently

• The code uses less memory

• The moving averages show improvement

• The code runs faster

How does the agent start its task in the game?

• From the right corner

• From the center

• From the left corner

• From a random position

What does the agent do when the pole leans to the right?

• Stays still

• Jumps

• Moves right

• Moves left

What is the purpose of rendering the screen in human mode?

• To reduce the complexity of the code

• To save memory

• To display the agent's actions

• To increase the speed of the game

What is the overall goal mentioned in the final section?

• To make the code more complex

• To improve the agent's balancing ability

• To reduce the number of episodes

• To simplify the rendering process