Reinforcement Learning and Deep RL Python Theory and Projects - Changing Policy Architecture

The video tutorial discusses various policy architectures in reinforcement learning, including MLP, CNN, and RNN policies. It provides guidance on selecting the appropriate policy based on the task, such as using CNN for image-related tasks and RNN for textual tasks. The tutorial demonstrates how to modify the MLP policy architecture by changing the number of layers and neurons. It also covers the process of training the model with the updated architecture using the PPO algorithm and evaluates the model's performance, highlighting the importance of saving the best model for future use.

Which policy is recommended for image-related tasks?

• None of the above

• RNN Policy

• CNN Policy

• MLP Policy

What is the default batch size for the MLP policy mentioned in the video?

• 128

• 256

• 512

• 64

How many layers are defined in the new MLP network architecture?

• 3

• 4

• 5

• 2

What is the purpose of setting 'verbose' to 1 during training?

• To increase the training speed

• To get detailed statistics

• To reduce memory usage

• To enable GPU acceleration

What is the average reward obtained after 20,000 steps?

• 180

• 150

• 220

• 200

Where is the trained model saved?

• In a zip format

• In a JSON file

• In a text file

• In a database

What is the next topic hinted at the end of the video?

• Modifying the algorithm

• Improving the reward function

• Changing the policy architecture

• Adding more layers