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

The video tutorial discusses the initialization and execution of a Q table in a learning algorithm. Initially, the old Q table is mentioned, and the process of reinitializing it with zeros is explained. The tutorial then demonstrates running the Q table and compares the results with previous runs, concluding that there is minimal difference in performance.

What was the initial state of the Q-table before re-instantiation?

• It was initialized with ones.

• It was not re-instantiated.

• It was empty.

• It was filled with random values.

What change was made to the Q-table before running the code?

• It was initialized with zeros.

• It was left unchanged.

• It was initialized with ones.

• It was filled with random values.

What was the observed performance after re-initializing the Q-table?

• Significantly improved.

• Slightly worse.

• Significantly worse.

• Almost unchanged.

How many times was the Q-table trained in the previous setup?

• 5,000 times

• 10,000 times

• 20,000 times

• 15,000 times

What was the approximate performance percentage after running the code?

• 75%

• 80%

• 85.9%

• 90%