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 queue table before it was re-initialized?

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What was the purpose of commenting out the print line for the queue table?

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What was the percentage of the run after the changes were made to the queue table?

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How many times was the Q table trained previously?

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What difference was observed after re-initializing the queue table with zeros?

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