Reinforcement Learning and Deep RL Python Theory and Projects - Solution (Number of Episodes)

The video tutorial discusses the concept of convergence in Q tables, particularly in the context of episodes in reinforcement learning. It explains that while the maximum number of episodes is unknown, convergence can be determined by observing when the Q table values stabilize over several episodes. The tutorial provides a method to calculate convergence by comparing the average of recent episodes with a reference point. It also includes a pseudo code example for implementing a convergence check, emphasizing the importance of choosing appropriate hyperparameters based on the task's criticality.

Describe the process of calculating the average of the Q table contents after a set number of episodes.

How does the concept of 'almost' play a role in determining convergence?

What factors should be considered when choosing the threshold for determining convergence?