We see an overview of reinforcement learning and AWS Deepracer on my first post of this series. Then we look one of basic framework for reinforcement learning, which MDP on the last post. Today, we will learn on how to build our first reinforcement learning model using AWS Deepracer Console.
Reinforcement learning is one of machine learning algorithm where we build a model (an agent) that can learn by itself from its interaction with the environment to reach some goals. There are lots of example of reinforcement learning agent, some of them are:
On the first part of this series we take a high level approach of what is reinforcement learning and take a helicopter view on AWS Deepracer Console. Now we will take a closer look on how to approach reinforcement learning concept mathematically.
In the early 20th century, a Russian mathematician, Andrey Markov, learned stochastic processes with no memory, later know as Markov chain. It is a statistical model of the sequence of process where the prediction probability of the next state of event is solely depend on the current state. For example, we want to predict the weather, right now…
A journey to learn reinforcement learning.
JML x AWS DeepRacer Bootcamp Pt. 1
You’ve probably have heard about self driving car. Maybe you’ve ever wondered how could it be possible. If you want to know some, than let’s deep dive into this article.
There are of course lots of parts that make autonomous driving can happens and it is still growing. One technology that make this self-driving car possible is the rise of computing technology using one of artificial intelligence (AI) technology which is reinforcement learning (RL).
So what is artificial intelligence? Is it like in Terminator movie? …
Data science enthusiast who curious about technology and how it's implemented in real life.