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 primary role of a policy network in a Deep Q Network?

• To initialize states

• To predict Q values

• To execute actions

• To store experiences

What is the main purpose of replay memory in DQN?

• To store the neural network weights

• To keep track of the learning rate

• To store experiences for learning

• To initialize the policy network

In the context of DQN, what does executing an action in the environment result in?

• A new policy network

• A reward and a new state

• An updated replay memory

• A new target network

What is the purpose of preprocessing the batch from replay memory?

• To increase the batch size

• To enhance the learning rate

• To prepare data for the policy network

• To initialize the target network

What is the relationship between Q values and target Q values in DQN?

• Both are derived from the policy network

• Q values come from the policy network, target Q values from the target network

• Both are derived from the target network

• Q values are used to initialize the replay memory

What is the purpose of using gradient descent in the policy network?

• To initialize the target network

• To minimize the loss

• To increase the batch size

• To maximize the reward

Why are the weights of the policy network copied to the target network after certain time steps?

• To stabilize the learning process

• To initialize the replay memory

• To increase the reward

• To reset the learning rate