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.

What is the role of the PPO algorithm in the context of the MLP policy?

How can the architecture of a policy be changed according to the text?

What were the results after training the model for 20,000 steps?