Check Out What Is The Next Big Thing In The World Of Artificial Intelligence

A kind of behavioral psychology of getting something to act or to work a certain way so it maximizes its rewards has inspired a new artificial intelligence approach called reinforcement learning. Named among the top ten breakthroughs of this year, this revolutionary machine learning enables computers to learn new things with no human intervention, via the mere act of experimenting and the right software design steps. Aside from boosting self-driving cars, the technology could get a robot grasp objects it never has seen before and it could determine the optimal configuration for the equipment in a data center. Simply, it is the closest humanity has so far gotten to true artificial intelligence.


With the wide availability of reinforcement learning that is expected to arrive in perhaps a couple of years, there is no better time to be familiar with the technology. The easiest way of doing so would be via the Reinforcement Learning Bundle, which is a set of four online courses that cover core AI concepts, including Bayesian machine learning in Python, A/B testing, generative adversarial networks, deep learning and neural networks applications and introduction to reinforcement among others of course with the use of software development tools.


Reinforcement learning is one of the most active research areas in AI. It is a way of training through rewards and punishments. A computer would be trained like a dog. If the dog obeys and acts in accordance with instructions, it is encouraged by giving biscuits. Otherwise, it would be punished by scolding or beating. In the same way, the system works well when the teacher gives positive value, such as a reward or the teacher gives negative value, like punishment for instance. The learning system that gets the punishment should improve itself. So, it is a trial and error process. The reinforcement learning algorithms retain the outputs selectively, which maximizes the reward received over time. To accumulate plenty of rewards, the system should opt for the best-experienced actions. Nonetheless, it has to try new actions to discover better action choices for the future.


This is a central data to reinforcement learning. It’s based on Monte Carlo methods as well as dynamic programming. It’s an unsupervised technique. The methods could directly learn from raw experiences with no model of the dynamics of the environment. Some examples include learning to play games, elevator control, robot control, animal learning and network routing.


One of the software that’s designed to provide traveling information as per the interests of users is the Personalization Travel Support System. It applies reinforcement learning to analyze as well as learn customer behaviors and list out products which the customers wish to purchase. If the system chooses the right item that the customer wants to buy, then it is given a reward through assigning a certain value for the state that a user chooses to do and if the system chooses an item that the user does not wish to purchase, it is then given a penalty. This way, the system learns personal interests. In the process, the system will acquire the knowledge of user behavior and interest, which makes it decide which or what information should be given to a specific user. This will result in greater customer satisfaction and growth in product promotion success rate.

Reinforcement learning is a kind of Machine Learning algorithms that enables software agents and machines to determine the ideal behavior within a particular context automatically, to maximize performance. Reinforcement learning is concerned about the problem of looking for suitable actions to take in a certain situation to maximize a reward. Reinforcement learning isn't given explicit goals. Rather, they’re forced to learn the optimal goals through trial and error. Consider the classic video game, Mario Bros., reinforcement learning algorithms will, through trial and error determine certain movements and button pushes will advance the game standing of a player and trial an error aims to result in an optimal gameplay state.


The algorithms that are generally used in the RL are created under various assumptions than those that are used in for instance, supervised learning tasks. A big difference is that the RL approaches don’t assume observed samples. If controlling something, such as a car, what is going to be observed five seconds later is highly correlated to what one does and observe presently. At times, one could simplify the problem, some can’t. Furthermore, reinforcement learning has the concepts of states, something that other popular frameworks do not have. When it comes to applications, to cast Bandits in a reinforcement learning framework, they could be used for various apps, such as web search, clinical trials, internet advertising and more.

The word of artificial intelligence indeed continues to build solutions and facilitate systems that more and more simplify and streamline processes, not just in business, in company processes but also on people’s daily lives.