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

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

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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.

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OPEN ENDED QUESTION

3 mins • 1 pt

What new insight or understanding did you gain from this video?

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