Reinforcement Learning and Deep RL Python Theory and Projects - SARSA Implementation

The video tutorial covers the implementation of Q-Learning and Sarsa algorithms, focusing on their differences and code structure. It begins with an introduction to Q-Learning, followed by a detailed walkthrough of the code. The tutorial then transitions to Sarsa, explaining how to modify the Q-Learning code to implement Sarsa. Finally, it compares the performance of both algorithms, highlighting that the effectiveness of each depends on the problem context.

What is the primary indicator in the code that Q-learning is being used?

• The use of a nested loop

• The use of a reward function

• The initialization of epsilon

• The presence of a maximum state value

In Q-learning, what determines whether to explore or exploit?

• The maximum number of steps

• The reward function

• The value of epsilon

• The number of episodes

What is a key difference between Q-learning and Sarsa?

• Q-learning updates rewards differently

• Q-learning does not use loops

• Sarsa requires a new action for the next state

• Sarsa uses a different reward function

What is the first step in implementing Sarsa after obtaining the new state?

• Update the reward

• Calculate the maximum value of the new state

• Select a new action

• End the episode

How did the performance of Sarsa compare to Q-learning in this example?

• Sarsa did not perform well at all

• Sarsa performed significantly better

• Q-learning performed slightly better

• Both performed equally well

What factor is crucial in deciding whether to use Q-learning or Sarsa?

• The initial state

• The number of episodes

• The specific problem being addressed

• The value of epsilon

What was the approximate success rate of Q-learning in this example?

• 80%

• 90%

• 88%

• 85%