AI Agents



How Can Reinforcement Learning Help Your Business


Introduction In the disruptive landscape of the 21st century, business as we know it has changed. With automation and Artificial Intelligence (AI) taking the front seat in industrial as well as corporate processes, the concept of doing business, as a whole, is being revamped. In such an environment that sees major upheavals every week and trends reversing overnight, the whole concept of taking your business forward depends on how tech-savvy your company is. And if we talk about tech, it is undoubtedly AI that has truly captured the imagination of everyone, the world over. While we are still decades away from the general purpose of AI that is depicted in sci-fi classics, AI has already pervaded our lives in myriad ways. From the personal assistant on our smartphones to the self-driving cars which promise greater road safety, AI is already a part of our lives. No talk of AI can be complete without touching on the subject of reinforcement learning, which, along with deep learning and machine learning, stands as one of the three pillars which support the foundation of AI itself. But what is this reinforcement learning, and how can it help your business? These are the questions, and more, that we are going to address in this article. So stick around for an interesting ride, and read on below.

What Is Reinforcement Learning? Before delving deeper into the chasms of reinforcement learning, it’s best to familiarize oneself with the concept. So what is reinforcement learning? Formally, it can be defined as a means of training an AI agent through the results of its experiences with the interacting environment. What does all this mean? Let’s expatiate using a fictitious example. Suppose you have an alien friend, who is from another planet (!), and you want to teach her how to handle fire, because she has never seen the fire before (she comes from an icy planet covered in permafrost). But sadly, you both cannot speak a common language. Then how do you teach her about fire? Reinforcement learning, in this case, would work by just letting the alien interact with the fire. She will feel the heat, learn how to handle the flame, and eventually, she will become an expert at working with fire. In winter, she will learn to benefit from the heat of the flame, while she'll also learn not to place her hands in it because she might get burnt. Therefore, through a repeated process of interacting with the environment, the alien being will learn when it will be rewarded by the flame and when it will be scalded. This is exactly how reinforcement learning works. By allowing the agent to interact with the environment, the instructor only sets the rules of rewards and setbacks, without any explicit instructions as to how the task is to be accomplished. It is the responsibility of the AI agent to figure out the best way to maximize reward and reach its desired goal. Reinforcement learning can be of two types, positive reinforcement, and negative reinforcement. This form of machine training has immense applications in robotics, industrial automation, and data science. In the following sections, you'll get to know about the benefits of reinforcement learning in an industrial setting.

Benefits Of Reinforcement Learning For A Company Reinforcement Learning can help businesses in a number of ways. From streamlining the manufacturing system to optimizing customer acquisition and retention, the possibilities that Reinforcement Learning brings to the table are virtually limitless. The following are three industry examples of reinforcement learning in action.
Reinforcement Learning And Medicine In the field of medicine, reinforcement learning can have far-reaching ripples. Algorithms can access vast amounts of clinical data and come up with correlation patterns that human analysis, no matter how in-depth, can scarcely hope to turn up. This process can be utilized to recommend a dynamic treatment regimen for diseases such as cancer, which require prolonged treatment.
Reinforcement Learning And E-Commerce Reinforcement learning can be put to excellent use in the field of e-commerce. From developing dynamic pricing systems that adjust as per the demand and supply curve to providing customers with targeted personalization, reinforcement learning systems can change the very way in which online shopping is carried out.
Reinforcement Learning And Investment The concept of reinforcement learning can be applied to financial systems in order to streamline the process of investment. AI agents can be trained to predict stock market trends, and investment decisions can be made smarter, if not downright predictable. The above three applications of reinforcement learning only constitute the tip of the iceberg when it comes to this technological marvel. The roots of the technique go deeper than mere surface-level AI, and applications are only limited by the users' imagination. Different Flavors Of Learning (And Their Corporate Applications) Now that we are fairly knowledgeable in the concept of Reinforcement Learning (called RL fondly) let's explore the different flavors of this wonderful technique.

Benefits Of Reinforcement Learning For A Company Reinforcement learning algorithms can be of multiple types, such as Q-Learning, Monte Carlo, and SARSA, to name a few. All RL methods can be broadly classified in one of two types, as mentioned above, and as explained below. Positive Reinforcement Positive reinforcement is a form of reinforcement learning in which the acting agent is rewarded for exhibiting certain desirable behavior. This is usually in the form of a score or some other stimulus that encourages the agent to perform its task better. Positive reinforcement is also widely used in commercial applications such as rewarding customers with a discount for purchasing a target article. Negative Reinforcement In this second type of reinforcement learning, instead of encouraging the agent with a positive stimulus, the agent is treated with the removal of a negative stimulus as a reward for successfully completing the desired operation. As with positive reinforcement, negative reinforcement, too, can be explained in the context of commercial applications; for example, people are encouraged to wear helmets while riding motorcycles so as to avoid head injury by accidents. The above two methods of reinforcement learning stem from common intelligent behavior, which naturally seeks to improve performance by increasing reward or mitigating risk. But we have talked enough about the theoretical implications of reinforcement learning. Does it work in the real world? You bet it does. Let’s see one of the prime examples of reinforcement learning in action. AlphaGo-Born Of RL

Industry Example | Alpha Go AlphaGo is an AI developed by DeepMind Technologies, a current subsidiary of Google. AlphaGo was primarily designed to play the complex game of Go. What differentiates Go from other human games like chess is that Go can have an infinite number of possible combinations of moves. This makes creating an AI that can effectively tabulate the movements of a Go game extremely difficult, as the environment cannot be entirely mapped by any existing data structure. This is where RL comes in. Using RL techniques in the form of a Monte Carlo algorithm running on the backbone of a deep neural network, the AlphaGo program was able to play the game of Go, repeatedly learning from its own mistakes, till it became a veritable master of the game. AlphaGo’s prowess in the game was proved to the world in March 2016 when it successfully beat Lee Sedol, considered the world’s best Go player. Thus by defeating a human master at his own game, AlphaGo has proven the efficacy of the reinforcement learning technique. AlphaGo is succeeded by more powerful programs such as AlphaGo Zero and AlphaZero. Now, imagine the potential business applications. Just like Go, the possibilities are endless! Contact SDS

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