DQN PyTorch Beginners Tutorial #6 – Explain Loss, Backpropagation, Gradient Descent

In this tutorial, we will cover the concepts of Loss, Backpropagation, and Gradient Descent in the context of Deep Q-Networks (DQN) using PyTorch.

Loss

Loss is a measure of how well our model is performing on a given task. In the case of DQN, the loss function aims to minimize the difference between the predicted Q-values and the target Q-values. Common loss functions used in DQN are Mean Squared Error (MSE) or Huber loss.

Backpropagation

Backpropagation is the process of calculating the gradients of the loss function with respect to each parameter in the model. This is done using the chain rule of calculus to propagate the error backwards through the neural network layers. Backpropagation allows us to update the model parameters efficiently using optimization algorithms like Gradient Descent.

Gradient Descent

Gradient Descent is an optimization algorithm used to minimize the loss function by iteratively updating the model parameters in the direction of the negative gradient. This helps us find the optimal set of parameters that will minimize the loss and improve the performance of our DQN model.

By understanding and implementing Loss, Backpropagation, and Gradient Descent in our DQN model, we can train our agent to effectively learn and improve its performance in various environments.