What on earth is this Reinforcement Learning?

Reinforcement learning, in psychological terms, means learning particular actions and behaviors by positively or negatively reinforcing the action or behavior. For example, if a child throws a tantrum in a shop, his parent rebukes. If the child behaves, however, the parent rewards him by giving him a cookie. This is reinforcement learning in action. The field of Reinforcement Learning or RL in machine learning tries to imitate this sort of learning into machines, as a way of teaching them more efficiently than other present algorithms. …

Actor-Critic Based Methods are now becoming the standard for policy-optimization reinforcement learning and even value-based reinforcement learning. Often in beginner tutorials and guides, actor-critic methods are explained and implemented for discrete action spaces. Reading complicated codebases like stable-baselines, spinning-up leaves you with no knowledge of how they actually port these algorithms over to continuous action spaces, and even if they do, it's most likely you have no idea how or why that works. So that’s what we’re covering in this blog post.

Note: This post is best suited for the beginner in reinforcement learning. You can find excellent resources online…

Credits: Michael Dziedzic on Unsplash

Deep Q Learning is a popular Reinforcement Learning Algorithm that has blown up in popularity over the past 7 or so years. The original paper came out in 2013, released by Google’s DeepMind. In this post, we will go into detail about how this algorithm works intuitively, and how YOU can implement it in 20 minutes.


  1. You are going to need to have some knowledge of Neural Networks and how they work. There are lots of guides online that will help you out with those.
  2. If you want to follow along with the code on this post, then you need…


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