Reinforcement Learning and Deep RL Python Theory and Projects - Moving Avg Implemented

The video tutorial explains the concept of moving average, particularly in the context of solving problems using DQN. It provides a detailed example of calculating moving averages over episodes and demonstrates how to implement this in code. The tutorial also covers testing and debugging the code to ensure accuracy and functionality.

What is the minimum moving average required over 100 episodes to consider a problem solved?

• 150

• 175

• 200

• 195

In the example provided, what is the moving average after the second episode with rewards 3 and 2?

• 3.5

• 3.0

• 2.5

• 2.0

What library is used to implement the moving average function in the tutorial?

• TensorFlow

• Keras

• NumPy

• PyTorch

What is the purpose of unfolding a tensor in the moving average calculation?

• To increase the tensor size

• To create a list

• To create a square matrix

• To flatten the tensor

Why are zeros concatenated at the beginning of the moving average array?

• To ignore initial values before the period

• To match the length of the original data

• To handle missing values

• To improve computation speed

What is the main reason for converting the moving average to a NumPy array?

• For easier debugging

• For visualization purposes

• For compatibility with other libraries

• For faster computation

What error is encountered when testing the moving average function with random values?

• Kernel crash

• Dimension mismatch

• Type error

• Syntax error

What is the period used in the example when testing the moving average function with random values?

• 100

• 3

• 10

• 5

What is the expected output for the first 99 values when the period is set to 100?

• Ones

• Averages

• Zeros

• Random values

What will be covered in the next lecture following this tutorial?

• Introduction to reinforcement learning

• Error handling in PyTorch

• Plotting the visualization

• Advanced moving average techniques