How computer programs are trained to think like humans without expert supervision
Okay, so the fast-evolving technology has another ‘formidable offspring’, Reinforcement Learning. It’s so astounding in its functionality that, without a doubt, it’s one of the breakthrough technologies of 2017, transforming several industries across the world. So, that makes it worthy to be called so.
Let’s see what exactly RL (Reinforcement Learning) is, and how is it impacting today’s enterprises.
What is Reinforcement Learning?
Reinforcement Learning is a branch of Artificial Intelligence and is classified as a type of a Machine Learning. Now, the aim of Machine Learning is to produce ‘agents’- meaning intelligent programs. These intelligent programs are produced through a series of learning and evolving strategies. And Reinforcement Learning is one of the methods that Machine Learning takes to accomplish in producing these agents.
To simplify it further, the aim of these intelligent programs is to mimic the learning methods of humans (or animals). By nature, we humans are equipped with direct sensory connection with our surroundings (our environment), which means we are directly interacting with the environment and getting a result through our actions. This interaction is also known as ‘cause and effect’. Through this cause and effect we build knowledge about how to interact with the environment and obtain the best results that suits us.
Now, Reinforcement Learning is all about giving that human quality (the power of observation and exploration) to robots to maximise their performances. In simpler terms, it’s all about making robots more human-like, enabling them to understand the environment, perform right actions, and obtain desired results through the power of effective decision-making skills.
The flow of Reinforcement Learning
1. The agent (intelligent program) receives the input details
2. The agent determines an action through decision-making
3. It performs the action
4. For every right action, the agents receives a reward
5. Then each reward given to the agent is recorded
There are two types of learning, supervised and unsupervised. So far programmers were dabbling with supervised learning, giving specific instructions on how a task has to be performed on each level. This proved to be difficult and time-consuming. Whereas, Reinforcement Learning is an unsupervised learning. The program (agent) develops its own knowledge and decision-making skills to perform right actions through constant analysis of what it right and what is not. The rewards given to these programs aids in identifying right decision-making.
The challenges of Reinforcement Learning
Just like every other technology, the Reinforcement Learning too has its share of challenges, which calls for an intelligent countermeasure, to emerge as a successful differentiation to today’s businesses. As we’ve discussed earlier, the Reinforcement Learning relies on exploitation and exploration to perform an action. If the agent is rewarded for a particular action, it determines that particular action is reward-worthy, and it repeats for more rewards. In essence, this is the agent exploiting on receiving rewards. But, what is also needed is the knowledge exploration, which will ultimately lead to better rewards.
In order to make the agent imbibe both – exploitation and exploration – it is imperative to infuse a variety of actions to strike a fine balance between the both, to achieve better outcomes.
Industries Reinforcement Learning is being used
Since Reinforcement Learning can operate without expert supervision, various industries are coming out with innovative methods to leverage the power of Reinforcement Learning. Recent reports say even Google is experimenting robotic functionality backed by Reinforcement Learning. No wonder Reinforcement Learning is creating such a buzz, and it’s getting louder.
Let us see some of the interesting examples of how Reinforcement Learning is shaping up industries today.
Imagine a robot picking up the right device from a lot and placing it in a box with great precision.
Fanuc, a Tokyo-based company has employed robots with Reinforced Learning to perform various tasks like picking up the right objects and placing it in a box with great speed and precision. They way they train these robots is pretty interesting. The AI team let the robots work the whole night to figure out on its own how to pick the right device. By morning, the robots – by receiving rewards for every right move – are all tuned up and ready to perform their tasks.
However, an infallible robot is not created overnight. But it’s well on its way, as the Reinforcement Learning experts are laboriously fine-tuning the technology to get the desired perfection they are aiming for.
Auto and transport
Startups specialising in car transportation is growing rapidly. Every time a new company shows up, it comes with an unique idea powered by solid technology. Uber, the car transportation giant is planning to come out with self-driving vehicles fuelled by Reinforcement Learning. To create an edge over their competition, it looks like Uber has some great plans up its sleeves to take car transportation to the next level.
Mobileye is a fine example of how RL is revolutionizing the transportation industry. This Israeli automotive company is pushing the limits of driverless cars. It’s vision system is guided by powerful machine learning programming coupled with intelligent softwares. The driver-less cars developed by Mobileye can identify people and other vehicles and maneuver accordingly. It can identify speed limit boards and is capable of adjusting its speed to match it. The back end system analyzes huge amount of data, accumulated over millions of miles of driving tests. These salient features make Mobileye stand out from its competitors, capturing the attention of computer mogul Intel. And it’s not hard see why Intel is offering $15.3 billion to acquire Mobileye.
With high inventories on hand, large warehouses are facing a daunting task of streamlining space. Today, these companies are dabbling with innovative ways to employ Reinforcement Learning to aid them in overcoming challenges in managing fluctuating demands of inventories and reducing transit time.
Offering ‘personalized shopping experience’ is at top of the must-have list of customer experience strategies in the retail industry. The industry is experimenting Reinforcement Learning to analyze customer behaviour and suggest/deliver products as per their specific needs.
RL programs will go through scores of customer preferences and recommend products and services that would more likely be interested in. This has benefited the e commerce in many ways, especially in customer interest analysis. Experts say that RL can considerably reduce analysis time and cut costs related to it.
The financial industry does not want to be left behind. It is leveraging the RL technology vigorously to evaluate key strategies in the trading paradigm. It is planning to evaluate and sketch a plan for continuous delivery of trading strategies.
Today, fintech startups are coming out with innovative ideas to transform untapped regions, and are banking on Reinforcement Learning to fulfil their startup dream.
Reinforced Learning is influencing human interaction to a greater degree. It appears that the time and effort we spend on programming is narrowing down to minimal. The way we take advantage of artificial intelligence is moving beyond the conventional sense to create solutions that can ‘think and work’ on its own. The concept of the learning curve of humans is slowly shifting to coded controllers and machines. Today’s industries and businesses are ready to embrace anything new with human touch, which can empathize with their potential customers.
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