This the second part of Reinforcement Learning (Q-learning). If you would like to understand the RL, Q-learning, and key terms please read Part 1.
In this part, we will implement a simple example of Q learning using the R programming language from scratch. It is expected from you to understand the basics of R programming and complete the reading of Part 1 of this article.
We are coding the algorithms using the R base package…Continue
Added by Nitin Agarwal on June 4, 2020 at 8:23pm — No Comments
Have you heard about AI learning to play computer games on their own and giving tough competitions to expert Human gamers?
A very popular example being Deepmind whose AlphaGo program defeated the South Korean Go world champion in 2016. Other than this there are other AI agents developed with the intent of playing Atari games like…Continue
Added by Nitin Agarwal on June 4, 2020 at 7:51pm — No Comments
Summary: For all the hype around winning game play and self-driving cars, traditional Reinforcement Learning (RL) has yet to deliver as a reliable tool for ML applications. Here we explore the main drawbacks as well as an innovative approach to RL that dramatically reduces the training compute requirement and time to train.
Added by William Vorhies on December 23, 2019 at 7:30am — No Comments
Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.
Imagine you’re completing a mission in a computer game. Maybe you’re going through a military depot to find a secret weapon. You get points for the right actions (killing an enemy) and lose them for the wrong ones (falling into a pit or getting hit). If you’re playing on high difficulty, you might not conclude this task in just one attempt. Try after try, you learn which consecutive actions are needed to get out of a location safe, armed, and equipped with bonuses like extra health points or…Continue
Added by Kateryna Lytvynova on April 18, 2019 at 2:00am — No Comments
Summary: This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet. This can be a guide to calming the hype. It can also be a roadmap to future opportunities once these barriers are behind us.
Summary: Which is more important, the data or the algorithms? This chicken and egg question led me to realize that it’s the data, and specifically the way we store and process the data that has dominated data science over the last 10 years. And it all leads back to Hadoop.
When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you’re like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don’t ease the choice.
In this article, I will try to explain basic concepts and give some intuition of using different…Continue
Added by Luba Belokon on October 26, 2017 at 6:00am — No Comments
Robotics has been and still is an enormous fascination for us humans. Even when we did not have computers, we were fascinated with Mary Shelley's Frankenstein because the concept of creating life out of nothing so resonates with us.
Fast forward to the modern word, making a…Continue
Added by Ammar A. Raja on May 26, 2017 at 9:00pm — No Comments