An introduction to Reinforcement Learning
by Thomas Simonini
Reinforcement learning is an important type of Machine Learning where an agent learn how to behave in a environment by performing actions and seeing the results.
In recent years, we’ve seen a lot of improvements in this fascinating area of research. Examples include DeepMind and the Deep Q learning architecture in 2014, beating the champion of the game of Go with AlphaGo in 2016, OpenAI and the PPO in 2017, amongst others.
In this series of articles, we will focus on learning the different architectures used today to solve Reinforcement Learning problems. These will include Q -learning, Deep Q-learning, Policy Gradients, Actor Critic, and PPO.
In this first article, you’ll learn:
What Reinforcement Learning is, and how rewards are the central idea
The three approaches of Reinforcement Learning
What the “Deep” in Deep Reinforcement Learning means
It’s really important to master these elements before diving into implementing Deep Reinforcement Learning agents.
The idea behind Reinforcement Learning is that an agent will learn from the environment by interacting with it and receiving rewards for performing actions.
Learning from interaction with the environment comes from our natural experiences. Imagine you’re a child in a living room. You see a fireplace, and you approach it.
It’s warm, it’s positive, you feel good (Positive Reward +1). You understand that fire is a positive thing.
But then you try to touch the fire. Ouch! It burns your hand (Negative reward -1). You’ve just understood that fire is positive when you are a sufficient distance away, because it produces warmth. But get too close to it and you will be burned.
That’s how humans learn, through interaction. Reinforcement Learning is just a computational approach of learning from action.
