Reinforcement Learning and Deep RL Python Theory and Projects - Final Structure Implementation - 1

The video tutorial covers the process of setting up an algorithm to manage episodes, initialize loops, and select actions using exploration or exploitation. It explains how to store experiences in replay memory and sample random batches for preprocessing. The tutorial also touches on the use of agent and environment manager classes to facilitate these processes.

How does the algorithm ensure that the actions are selected based on the exploration rate?

What is the process of sampling a random batch from replay memory?

Describe the role of the policy network in calculating the loss between Q values.

What is the function of the 'extract tensors' method in the algorithm?