REINFORCE Algorithm explained in Policy-Gradient based methods

Policy gradients Policy gradients is a family of algorithms for solving reinforcement learning problems by directly optimizing the policy in […]

Deep Reinforcement Learning: Using policy-based methods to play Pong from pixels

This is a long-overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can […]

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Evolution strategies (ES) is an optimization technique that’s been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on […]

Double DQN and Dueling DQN in Reinforcement Learning

In this part, we will see two algorithms that improve upon DQN. These are named Double DQN and Dueling DQN. But first, let’s […]

SumTree data structure for Prioritized Experience Replay (PER) explained with Python Code

Weighted sampling from a list-like collection is an important activity in many applications. Weighted sampling involves selecting samples randomly from […]

Shannon entropy and its properties

Suppose you are talking with three patients in the waiting room of a doctor’s office. All three of them have […]

Principal Component Analysis (PCA) Explained

What is PCA? Let’s say that you want to predict what the gross domestic product (GDP) of the United States will be […]

What are eigenvectors and eigenvalues?

Introduction Eigenvectors and eigenvalues have many important applications in computer vision and machine learning in general. Well known examples are PCA […]