Reinforcement Learning and Deep RL Python Theory and Projects - Solution (Alpha)

The video tutorial explores the impact of learning rate values on the convergence of Q-learning algorithms. It explains that a learning rate of zero results in no learning, as the new value remains the same as the old value. Conversely, a learning rate of one leads to instability, as the algorithm constantly updates with new values, neglecting the old ones. The optimal learning rate should be between zero and one, depending on the desired speed and stability of learning. A dynamic learning rate, which starts high and gradually decreases, can be used to balance the importance of new and old values over time.

Why does a learning rate of zero prevent convergence in Q-learning?

• It causes the new value to be the same as the old value.

• It makes the new value completely override the old value.

• It results in faster learning.

• It leads to random fluctuations in the value.

What happens when the learning rate is set to one in Q-learning?

• The values become more stable.

• The new value is ignored.

• The old value is completely replaced by the new value.

• The learning process becomes slower.

What is the recommended range for the learning rate to ensure convergence?

• Exactly zero

• Exactly one

• Between zero and one

• Greater than one

How can a dynamic learning rate benefit the Q-learning process?

• By setting the learning rate to zero

• By increasing the learning rate over time

• By decreasing the learning rate over time

• By keeping the learning rate constant

What is the effect of starting with a high learning rate and then decreasing it?

• It causes the model to forget old values.

• It leads to immediate convergence.

• It allows for initial fast learning followed by stability.

• It results in no learning.

Why is it important to adjust the learning rate over time?

• To ensure the model never converges

• To keep the learning rate constant

• To balance between learning new values and stabilizing old values

• To make the model learn only old values

What is a potential strategy for setting the learning rate in Q-learning?

• Increase it over time

• Set it to zero initially

• Start high and decrease it gradually

• Keep it constant at 0.5