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. For a deeper understanding of how this learning process compares to human cognitive abilities, exploring the differences and intersections of human intelligence and artificial intelligence can be illuminating.
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.
Reinforcement learning can be better understood with the help of its types.
There are two types of reinforcement learning – positive and negative.
Positive Reinforcement LearningPositive 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. For a deeper understanding of how human-driven feedback loops can enhance the efficacy of reinforcement learning, consider reading this insightful article on human-in-the-loop machine 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.
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.
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.
Reinforcement learning can be applied to various fields – marketing, healthcare, broadcasting, and robotics. Here are a few of the applications of reinforcement learning:
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:
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.
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, 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.
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.
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:
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.
As reinforcement learning is still in the improvement phase, it also has its fair share of limitations.
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.
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.
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).
Some common algorithms used in Reinforcement Learning include Q-Learning, SARSA, and Deep 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.
