Reinforcement Learning and Deep RL Python Theory and Projects - Neural Network Class Implementation

The video tutorial covers the implementation of a policy network using Python libraries. It begins with an introduction to neural networks and the necessary libraries, followed by the creation of a DQN class. The tutorial explains the components of the class, including the initializer and fully connected layers. Forward propagation is implemented using tensors and the Relu function. The video concludes with a summary of the DQN class and hints at future topics like replay memory and target networks.

How does the forward propagation function work in the DQN class?

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What is the significance of using ReLU as a nonlinearity in the forward propagation?

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Discuss how the DQN class can be used as both a policy network and a target network.

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