Reinforcement Learning and Deep RL Python Theory and Projects - DQN Algorithm Steps

The video tutorial introduces the concept of Deep Q-Networks (DQN), highlighting its similarities to Q-learning and Sarsa. It explains the roles of policy and target networks, the importance of replay memory, and the process of executing actions and receiving rewards. The tutorial also covers storing experiences, sampling from replay memory, preprocessing data, calculating loss, and updating weights using gradient descent. The video concludes with a discussion on hyperparameters and the overall goal of the module.

What is the role of experience in the context of deep Q-learning?

Discuss the preprocessing steps for graphical data in deep Q-learning.

How does the target network differ from the policy network?