randomized-value-functions

Noisy Linear Layer

A Noisy Linear Layer is a type of linear layer used in reinforcement learning networks to improve the agent's exploration efficiency. It is created by adding parametric noise to the weights of a linear layer. The specific kind of noise used is factorized Gaussian noise. What is a Linear Layer? Before delving into what a Noisy Linear Layer is, it's important to understand what a linear layer is in the context of neural networks. A linear layer refers to a layer in a neural network that perform

Random Ensemble Mixture

What is REM? If you have ever heard of machine learning or deep reinforcement learning, you may have come across a term called Random Ensemble Mixture (REM). But what is REM and how does it work? In simple terms, REM is an extension of the Deep Q-Network (DQN) algorithm for deep reinforcement learning inspired by a technique called Dropout. DQN is a popular algorithm in deep reinforcement learning that uses artificial neural networks to learn a policy that maximizes the expected reward in a gi

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