Reinforcement Learning Examples are given below in details
Most people follow a similar process when learning new things. That is, you receive information, process it, try it yourself, and get feedback on the results. Much of this process is also reinforced by rewards and punishments. If you answer correctly, you can earn a gold star, extra points, or a higher grade. If the answer is wrong, you will lose points, be eliminated from the competition, or have to repeat the exercise.
As artificial intelligence has become increasingly popular and capable, programmers have begun to use the same process in a popular form of machine learning called reinforcement learning (RL). This technology enables businesses to customize, control, and monitor their workflows with a level of accuracy and sophistication that was never possible before. As reinforcement learning evolves, its potential and benefits will only become stronger.
Read on to learn more about the origins and applications of RL. Many of which you may have already experienced today.
What is reinforcement learning?
Reinforcement learning is the closest to human learning that digital systems and machines can achieve. Through this training, machine learning models can learn to follow instructions, run tests, operate equipment, and more.
Reinforcement learning centers around digital agents that are placed in a specific environment to learn. As we learn new things, agents face game-like situations and have to make multiple decisions to achieve the right outcome.
Agents learn what to do (and what not to do) through trial and error and will be rewarded and punished accordingly. Every time a reward is received, it reinforces that behavior and prompts the agent to adopt the same strategy the next time.
Read more about reinforcement learning.
History & Background
The foundation of reinforcement learning was laid over 100 years ago, and it actually has two origins. The first is rooted in animal learning and the “law of influence” formulated by Edward Thorndike. Thorndike described the law of effect in 1911 as the concept that animals repeat behaviors that bring them satisfaction, but are discouraged by behaviors that bring them discomfort. Furthermore, the higher the level of pleasure or pain, the greater the pursuit or prevention of the behavior. The 4-effect law links both selective and associative learning. In preference learning, animals try several different options or routes and then try to choose one of them based on the outcomes. In associative learning, animals make choices based on how they associate themselves with the situation and whether it is positive or negative.
Although Thorndike established the essence of reinforcement learning, the term “reinforcement” was formally used by Ivan Pavlov in 1927. He described reinforcement as the reinforcement of a behavior pattern by the animal receiving a stimulus, or reinforcer, in a time-dependent relationship with some other stimulus or response. In other words, when an animal receives something but gets a response. Whether or not they do it immediately after doing it will affect whether or not they will do it the same way again in the future.
The second origin, optimal control, is rooted more in mathematics and algorithms than in animal learning. In the 1950s, researchers began to define optimization techniques to obtain control policies for continuous-time control problems. Based on this, Richard Bellman developed programming that uses the state of a dynamical system to define a functional equation and return an optimal value function (commonly called the Bellman equation). Berman then introduced the Markov Decision Process (MDP). He defines this as “a discrete stochastic version of the optimal control problem.”. MDP helps create solutions that gradually arrive at the correct answer to something through sequential guesses. This is similar to modern reinforcement learning.
Reinforcement Learning examples and applications
The field of reinforcement learning is growing, and it has a bright future. Here, we’ll examine a few of the real-world applications of RL at the moment.
1. Automated Robots
Most robots don’t look like the pop culture icons you would believe, but their abilities are just as impressive. The more a robot learns using RL, the more accurate it becomes and the faster it can complete tasks that were previously difficult. They may also carry out missions that pose a threat to people but have little consequence. For these reasons, in addition to requiring some level of monitoring and regular maintenance, robots provide a cost-effective and efficient alternative to manual labor.
For example, some restaurants use robots to deliver food to your table. Grocery stores are using robots to identify shelf shortages and order more items. In specialized environments, automated robots have traditionally been used to assemble products. Inspect for defects. Count, track and manage your inventory. We will deliver the product to you. I travel both long and short distances. Data entry, organization and report creation. Grasp and handle objects of different shapes and sizes. As we continue to test the robot’s capabilities, new features are introduced to enhance its functionality.
2. Natural Language Processing
Predictive text, text summarization, question answering, and machine translation are all examples of natural language processing (NLP) that use reinforcement learning. By studying specific language patterns, RL agents can mimic and predict how people speak every day. This includes not only the actual language used but also syntax (placement of words and phrases) and vocabulary (word choice).
In 2016, researchers at Stanford University, Ohio State University, and Microsoft Research used this learning to generate interactions used in chatbots. They simulated a conversation using two virtual agents and used a policy gradient method to reward important characteristics such as consistency, informativeness, and responsiveness. This research examines not only the question at hand but also how its answer will affect future results. This approach to reinforcement learning in NLP is now widely adopted and used in the customer service departments of many major organizations.
Read more about natural Language Processing.
3. Marketing and Advertising
Both brands and consumers can benefit from reinforcement learning. If you’re a brand selling to a targeted audience, you can use real-time bidding platforms, A/B testing, and automated ad optimization. This means you can run a range of ads in your market and the host will automatically deliver the best performing ads to the best locations at the lowest cost. Brands post and set up campaigns themselves, but marketing and advertising platforms also learn what types of ads appeal to their audiences and show them more often and more prominently.
From a consumer’s perspective, the advertisements they receive are typically from companies whose website they have visited before, have previously purchased from, or are in the same industry as the company they purchased from. You must have seen. This is because marketing and advertising platforms can use reinforcement learning to link similar companies, products, and services to prioritize specific customers. If you try a particular option and get clicks or other engagements, it means they were “right” and you should try the same strategy again.
4. Image Processing
Have you ever taken a security test where you were asked to identify an object in a frame, like “Click the photo with the road sign”? This is similar to what a learning machine can do, but the approach is different.
When asked to process an image, the RL agent searches the entire image as a starting point, identifying objects sequentially until all are registered. Artificial vision systems also use deep convolutional neural networks consisting of large labeled datasets to map images from simulation engines to human-generated visual descriptions.
Examples of reinforcement learning in image processing include:
- A robot equipped with visual sensors to sense its surrounding environment.
- Scanner to understand and interpret text.
- Image preprocessing and segmentation of medical images such as CT scans.
- Traffic analysis and real-time road processing with video segmentation and frame-by-frame image processing
- CCTV cameras for traffic and crowd analysis.
5. Recommendation Systems
Amazon’s “Frequently Bought Together” section, Target’s online “You May Also Like” tab, and news outlets’ “Recommended Reading” articles all use learning machines to generate recommendations. Meat. This is correct. I. Especially when reading news, the RL agent can track the types of articles, topics, and even author names that users like, so that the system can queue the next article. Something that users can enjoy. This includes details about how users interact with your content, such as clicks and shares, as well as aspects such as the timing and freshness of the news. Rewards are defined based on these user actions. five
Recommender systems can also predict future behavior by analyzing past behavior. For example, if 100 people buy ski pants and then ski boots, their system will learn to send ads for ski boots to people who just bought ski pants. If your ad isn’t successful, try running an ad for ski jackets instead and compare the results.
6. Gaming
From creating new games to testing bugs to clearing levels, RL is an efficient and relatively easy resource for programmers. RL models are much easier to train than traditional video games, which require complex behavior trees to build the game’s logic. Here, agents learn on their own through navigation, defense, attack, and strategy planning in a simulated game environment. Through trial and error, you begin to take the necessary actions to achieve your desired goal.
RL agents are also used for bug detection and game testing. This is due to the ability to execute large numbers of iterations without human input, stress testing, and creating potential bug situations.
7. Energy Conservation
Many countries around the world are working to reduce their impact on climate and reducing energy consumption is at the top of the list. Deep Mind’s partnership with Google to cool large and critical Google data centers is a prime example of this. With a fully functional AI system, the center has reduced energy consumption by 40% without the need for human intervention. However, there is still some oversight by data center professionals.
The system works as follows.
- Takes a snapshot of data from the data center every 5 minutes and feeds it to the deep neural network.
- Predict how different combinations will affect future energy consumption
- Identify actions to reduce power consumption while maintaining certain safety standards.
- Send these actions to the data center for implementation.
- Verification of operation by the local control system.
Other examples include Eco settings on your thermostat or motion-activated lights that offer different settings depending on the level of light already present in the room.
8. Traffic Control
Civil engineers have been plagued by transportation problems for centuries, and reinforcement learning is working to solve them. Continuous traffic monitoring in complex urban networks helps to create literal and figurative “maps” of traffic patterns and vehicle behavior. Due to its data-driven nature, the RL agent can begin to learn when traffic is heaviest, what direction traffic is coming from, and how fast cars pass by each light color. Then adapt accordingly, while continuing to test and learn according to time, climate, and season.
9. Healthcare
The healthcare industry uses machine learning and artificial intelligence in many of its tasks, and RL is no exception. It is used for automated medical diagnosis, resource scheduling, drug discovery and development, and health management.
An important means of introducing reinforcement learning is dynamic treatment planning (DTR). To create a DTR, a series of clinical observations and evaluations of the patient must be recorded. The learning system uses past results and the patient’s medical history to make suggestions about the type of treatment, drug dosage, and appointment time at each stage of the patient’s treatment. This is extremely beneficial for making time-sensitive decisions about the best treatment for a patient at a particular time without expending too much time, energy, and effort consulting with multiple stakeholders.
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