Reinforcement learning (RL) is a branch of machine learning. It is a machine learning agent that learns from its own experience. This branch of data science does not need to be fed data to perform tasks. RL aims at using no external data but learning from experiences within the environment through positive and negative behaviors to produce actionable results.
There are three types of machine learning: supervised, unsupervised, and reinforcement. Supervised machine learning is much similar to reinforcement learning but with a set of training data that is used to correct its own actions. Under unsupervised learning, there is no training data set, but the models discover insights from hidden data and patterns.
The goal of reinforced learning is to use no trained set of data or hidden data but to perform a task using the trial-and-error method.
Just like humans get reinforced to certain tasks with repeated actions, reinforced learning aims at learning from its own actions and outcomes.
Types of Reinforcement Learning
Reinforcement learning can be better understood with the help of its types. There are two types of reinforcement learning – positive and negative.
Positive Reinforcement Learning
Positive reinforcement refers to when an action results in a positive outcome. Any action made by an agent that increases overall performance within the environment is considered positive reinforcement. Positive behavior is added to existing machine learning models to act as a reward i.e., reinforcing it to achieve the same results again.
Negative Reinforcement Learning
Negative reinforcement learning is learning through negative outcomes and negative behavior. When the algorithm produces negative actions, negative behavior is reinforced in the form of punishment for the existing models to improve and perform better. The punishment (negative behavior) acts as a deterrent to minimize the negative behavior and sets a standard for positive behavior.
This, in turn, allows the agent to perform with optimization and maximize the total reward.
Examples of Reinforcement Learning
To understand the concept of reinforcement learning better, here are some real-life examples.
Do you remember Pavlov's conditioning theory based on a dog? Let's recall it as reinforced learning works in a similar manner.
Under the conditioning theory, Pavlov suggested training a dog requires a stimulus. This stimulus was ‘ringing a bell.’ However, just ringing a bell resulted in no response. Even presenting the food also didn't receive any response from the dog. However, whenever the bell was rung, and food was presented, the dog started salivating. Pavlov inferred that salivation was a learned response. Even without the food presentation, the dog responded with salivation every time the bell rang. Similar is the case of reinforcement.
The dog was conditioned and associated ringing the bell with food. When linked with reinforcement, the food acted as positive reinforcement.
The dog acts as the AGENT
Ringing the bell acts as a STATE (ACTION)
Food acts as a REWARD.
Depending on the use case, the reward can be positive or negative. A dog may also be punished, which will open doors for improvement. A dog may be rewarded, which will be considered positive reinforcement.
Application of Reinforcement Learning
Reinforcement learning can be applied to various fields – marketing, healthcare, broadcasting, and robotics.
Here are a few of the applications of reinforcement learning:
Reinforcement Learning in Marketing
Digital marketing can benefit a lot from reinforcement learning. Marketing is all about identifying the likes and dislikes of the target group and predicting their buying behavior to promote the products and services. Businesses have spent thousands on analytics and digital marketing campaigns to understand such trends.
Reinforcement learning and its capabilities can help marketers:
Personalize the product recommendations while shopping
RL can read buyers' action, predict the products that most likely suit their interest and preferences, and rewards the business with sales.
Keep their advertising budget optimized
Marketers have to spend thousands on advertising with no guarantee of getting an ROI.
Reinforcement learning assures a high investment return through personalized recommendations, real-time prediction, and better-reinforced architecture.
Finding the suitable advertising material
It isn’t easy for marketers to find the right advertising content that serves the goal.
Reinforcement learning can find the best advertising campaign based on its reinforced learning models.
Predicting customer’s reactions to price changes
Reinforcement learning is also helpful in identifying the possible ways customers will respond to price change.
As it’s capable of forecasting buyers’ purchasing behavior, the agent can also find the number of customers that will welcome the idea while others may ditch it.
Reinforcement Learning in Broadcasting
Broadcasting and journalism are also benefiting largely from reinforcement learning. Through negative and positive reinforcement, it’s easier to identify the reader’s behavior toward the news content.
The audience has become more expressive. They have many means to showcase their thoughts on a given subject. This has kept broadcasting media on their toes to fact-check news before releasing it. Reinforcement learning can help broadcasters to understand the need to use catchy headlines and predict users’ responses accordingly.
Reinforcement Learning in Gaming
Pro gamers can benefit from reinforcement learning by training the agent to meet unexpected challenges a normal gamer cannot. Reinforcement learning has been introduced to popular mobile games like Flappy Bird, Subway Surfers, and more.
Reinforcement learning has made playing these games more playable. Adding negative reinforcement like the deduction of coins and reduction in lives motivates the agent to improve the performance through the experience. Positive behavior is encouraged by rewarding with the help of coins. These games use a reinforcement learning technique called the Q-learning approach to train the agent.
Reinforcement learning has also been introduced to league games like Alpha Go and many others. AI in the gaming industry is growing rapidly.
Reinforcement Learning in Healthcare
Reinforcement learning, when utilized in healthcare, can make saving lives easier. It can be used to diagnose diseases, suggest the best treatment, and identify the required doses and even the timings at which the doses should be administrated for the best results.
RL uses DTRs, Dynamic Treatment Regimes (one of the use cases of RL), for such purposes. It can also reduce the number of healthcare situations that go haywire due to delays in diagnosis. It can identify problems through its optimized and reinforced solutions.
It automates the process of decision-making required in existing treatments. Studies have also given insights into using deep reinforcement for sepsis treatment, chemotherapy, glycemic control in sepsis treatment, and more.
However, reinforcement learning in healthcare is yet to be tested in real-life situations.
Reinforcement Learning in Logistics and Supply Chain Management
According to studies, RL can be useful in inventory control and in case of disaster relief. RL can use historical data to predict the need for inventory ahead of time through its forecasting and optimizing approach. It’s also more feasible than other machine learning applications because RL requires an environment to interact with.
RL algorithms can also be used for delivering solutions. However, with the lack of research and applications, it won’t be wrong to say that RL isn’t feasible in handling complex multiagent systems (parties), as required in the case of logistics.
But RL in logistics is a powerful tool once more research methodologies are applied in the field.
Reinforcement Learning in Manufacturing
The main aim of manufacturing units is to produce products that meet the needs and wants of people. Manufacturers can use RL solutions to speed packaging, undergo quality testing and receive customer feedback faster. RL can use customer feedback correctly and incorporate the improvements within the manufacturing process. This can result in better product performance, product profitability, and an increase in sales margin.
Reinforcement learning can be inherited into manufacturing for:
Self-repairing of smart manufacturing systems (devices)
Product designing in textiles, drug, and alloy
Fermentation control in biotechnology
Fiber creation by making use of optimal strategies
RL can also be successfully implemented in case of job scheduling and dispatching of mass projects within manufacturing units. Many problems exist in job scheduling due to a lack of information and configuration issues. RL can handle these as negative behaviors and develop optimization techniques to reinforce positive results.
RL can also solve challenges involved in addictive manufacturing, product assembly, high-precision assembly, and more.
The list is not exhaustive. Reinforcement learning can be applied to many other realms like robotics, image processing, and hospitability.
Challenges involved in Reinforced Learning
As reinforcement learning is still in the improvement phase, it also has its fair share of limitations.
Infeasible with a lack of data
Reinforcement learning requires an environment. This can be a stimulated environment or a real-world environment. It has proved successful in a simulated environment, with the success of RL in gaming and robotics. Alpha Go Zero is a live example of the same. However, the results not that reliable in the real world.
Moreover, a stimulated environment has unlimited data that RL learning models can utilize to solve problems. This isn’t true or practical in other environments.
In short term, there is a lack of data which can hinder the performance of RL algorithms since the agent learns from data available within the environment.
Fails due to poor data logging
Reliable data logging is the heart and soul of reinforced learning projects. Any lag in logging the data results in wrong predictions, and that model can fail miserably. Langford, a reinforced learning researcher, mentions, “There’s a strong failure mode associated with seemingly minor logging failures.”
Often, engineers mistake the actual feature of the model with reference features. When it’s time for training models, the corrupted information breaks down the system.
Difficult to choose reward structures
Who is rewarded? In reinforcement learning, there has to be some form of reward for the agent to perform well. It’s simpler to set rewards in some cases but difficult in other circumstances. In the case of a mobile game, a reward can be linked to scoring a point or earning coins.
But in the case of marketing, it’s much more complex. For instance, when using RL for advertising, say, to predict the number of ads to place on the website, the reward must be tied to the revenue generated per event. In this case, it would place ads all over the websites, as more ads mean more revenue. However, a website full of ads would turn out to be catastrophic.
Thus, aligning rewards with action is one of the downfalls of using RL. RL works well where rewards can easily be tied with the action, such as tying sales as a reward to the predicting buyer’s product recommendations.
Reward shaping takes too much time
As reward is the key element in reinforcement learning, setting and framing rewards sometimes takes way too much time. Reward shaping is done with the help of complex mathematical functions, requiring a lot of intervention to add rewards before an action is completed.
In actual sense, it’s more complex than it sounds.
Takes too long to identify sample inefficiencies
The AlphaGoZero, nearly self-played more than a million times before it could figure out how to reach its final goal in the game.
The primary motive of reinforced learning is to train an agent in a way to find sample inefficiency and makes its way forward after addressing it. Sometimes, this process becomes too lengthy.
Guss, a research scientist at OpenAI mentions, “By the time you engineer a reward function that gives you a good signal at every time step, you basically solve the task.”
Lack of resources
Implementing reinforcement learning into a system requires a high level of computing power. From high-level research labs to computing systems with high GPU capabilities, RL needs to have proper resources before it can be put into practice.
A state school or even a university does not have such resources for RL, thus acting as a limitation.
Reinforcement learning is a step towards revolutionizing the existing data. RL has the potential to perform just with the help of data without any knowledge of dynamics or analytics. This agent and reward system learns from its own environment and experience to predict behaviors – be it in the field of finance, marketing, advertising, gaming, robotics, or broadcasting.
FAQs on Reinforcement Learning
How does Reinforcement Learning work?
In reinforcement learning, an agent interacts with an environment by selecting actions based on its current state. The environment responds to the agent's actions with rewards or penalties, and the agent updates its policy based on the received feedback. The goal is to learn a policy that maximizes the expected total reward over time.
What are some applications of Reinforcement Learning?
Reinforcement learning has been successfully applied to a variety of problems, including game playing (e.g., AlphaGo), robotics (e.g., controlling a robotic arm), autonomous driving (e.g., navigating a car), and recommendation systems (e.g., suggesting products to customers).
What are some common algorithms used in Reinforcement Learning?
Some common algorithms used in Reinforcement Learning include Q-Learning, SARSA, and Deep Reinforcement Learning.
What is the difference between Supervised Learning and Reinforcement Learning??
In supervised learning, the model learns to make predictions based on labeled data, while in reinforcement learning, the model learns to make decisions based on feedback from an environment. Supervised learning is typically used for tasks such as classification and regression, while reinforcement learning is used for tasks such as control and decision-making.
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