Reinforcement learning, commonly known as a semi-supervised learning model in machine learning, is a method for allowing an agent to gather environmental information, perform actions, and interact with the environment in order to achieve maximum total rewards.
Agents are trained based on a reward and punishment mechanism. The agent is rewarded for the right moves and punished for the wrong ones. Consequently, the machine makes the best possible decision.
In this article, let’s look at reinforcement learning in detail to see its real-world applications and use cases.
Some components used in reinforcement learning are:
|Environment||It is the physical space into which the agent is deployed.|
|State||It represents the situation of the agent.|
|Reward||It is the feedback given to the agent.|
|Policy||It is a set of rules to map the agent’s state to actions.|
|Value||It is the reward obtained by the agent.|
How Does Reinforcement Learning Work?
Reinforcement learning problems involve learning what to do next and how to map different situations to actions to maximize a numerical reward signal. The agent performs this by interacting with its environment to maximize rewards.
The model usually incorporates a trial and error method to solve a particular problem. The initial state is selected randomly. The feedback received from one state guides the agent to the next state.
To understand it better, we take the example of parenting. Suppose your child scores well on a Math test, you reward them with chocolates, to encourage good scores. This method is called positive reinforcement.
On the other hand, if the child scores poorly, you punish the child with a motivation to discourage such scores in the future. This method is called negative reinforcement. This way, the child learns from the mistakes and will make more informed decisions in the future.
Why Reinforcement Learning?
- As it is highly adaptive in nature, we can use these models to build solutions for complex problems where naive approaches might not work effectively.
- The algorithm continuously learns in real-time, with every decision being better than the previous ones. This improvement is observed because we gain experience while making each one. This method makes the decision-making process more dynamic and effective.
- The model learns with new live data more inclusively, unlike other machine learning models, whose accuracy can depend upon the data used for training it.
Applications of Reinforcement Learning
Let’s look at the main real-life applications of reinforcement learning.
Gaming has become a platform for more than just entertainment. Training a virtual agent to outperform human players can teach us to optimize different processes in different exciting subfields.
Reinforcement learning is so common nowadays because it is the mainstream method used to solve problems in game development and sometimes achieve superhuman performance. Google has developed an AI named Google DeepMind, which learns how to play games and even optimize the industry’s operation efficiency using reinforcement learning.
Google DeepMind with its popular AlphaGo was able to beat the best Go player in history and scored a goal that was considered impossible at that time.
2. Self-driving Cars
Initially, self-driving cars used supervised learning to train the model. But it had its drawbacks. Human bias was present throughout the AI process, from dataset preprocessing to model deployment.
Reinforcement learning is the perfect solution to verify that a self-driving automobile has learned all conceivable scenarios and is capable of handling any situation safely.
Companies like Tesla Motors have honed this technology to a point where it is now part of our everyday lives. A type of reinforcement learning called the Markov model has found its application in self-driving cars.
There are multiple challenges faced while designing a self-driving AI:
- Pedestrian detection and avoiding ghost detection (detecting an entity even when it is not present)
- Traffic sign recognition
- Maps and routes
- Handling emergencies in the best possible way with minimal damage incurred
3. NLP Applications
Most of us use auto-complete on our mobile phones or PC while typing text. Reinforcement learning is widely used in these NLP-related fields to personalize the autocomplete recommendation. When a word is typed repeatedly, it goes higher on the recommended list.
Some other tasks done by reinforcement learning in NLP are:
- Text summarization
- Answering questions: Personal assistants like Google Assistant and Alexa use reinforcement learning to answer queries and combine with NLP to understand requests and deliver appropriate results.
4. Digital Marketing
Reinforcement learning has found its application extensively in digital marketing. Personalized video or product recommendation works on the principle of reinforcement learning.
Some areas where we use reinforcement learning to improve digital marketing are:
- Product recommendations on Amazon
- Video recommendations on YouTube
- Personalized playlist creation on Spotify, Wynk, and other music streaming platforms.
Example: Sheldon is searching for a YouTube video on “How to invest in stocks”. While searching, he sees an ad for an application related to stock trading, so he checks the app. Later that day, he decides to read some online recipes on “How to make pasta”, interestingly he gets an ad on trading again.
How does this happen? The ad system has recognized his name in the earlier advertisement as a person interested in trading and has directed him to a similar recommendation.
Sheldon now decides to buy himself a keyboard on Amazon. When he reaches the purchase page, he finds that the website is giving him a recommendation of a mouse as ‘Items bought together with: ’. This is another application of Reinforcement Learning because the recommendation system has observed people buying the mouse and keyboard together. Now Sheldon has a thought of buying both.
Sheldon likes to watch sci-fi movies, so he opens his Netflix app. He discovers a column called “recommended for you”. It contains some famous sci-fi films he liked. It is another application of Reinforcement learning to personalize movie recommendations based on Genre, Actors, Director, or any common feature among your previously chosen/liked movie.
5. Industrial Applications
Automation in manufacturing
In industry, reinforcement learning-based robots perform various tasks. Apart from the fact that these robots are more efficient than human beings, they perform dangerous tasks that risk human life. Since the usage of Reinforcement Learning results in continuous learning, changes in work do not affect the performance.
Applications: Food, a robot built by Japanese robot developer RT, has an image recognition system that captures the food in front of them. Then, the fork-shaped robotic arm will pick up the noodles on the green conveyor belt.
This conveyor belt weighs the noodles and then transfers them to the packing box. The robot can pick up to 500g of noodles or other foods. The robots can help cut labor costs and ensure better social distancing among employees.
Reduction in energy consumption
By applying DeepMind’s (a deep learning AI developed by google) machine learning to Google cloud data centers, Google has reduced the energy consumption for cooling by up to 40 percent. The application can extend to any large-scale industry, and it can be phenomenal in improving the output.
- The AI continuously takes snapshots of the data stored in cloud storage.
- It observes the energy used for different forms and formats of data.
- It optimizes the storage to achieve the maximum possible energy conservation, improving efficiency and progress towards green computing.
6. Healthcare for Disease Prediction and Treatment Recommendations
The healthcare industry has always been an early adopter of the latest technologies and a big beneficiary of the adoption. Unlike traditional supervised learning systems, which rely on one-time, complete, and supervised reward indications, reinforcement learning evaluates and performs a delayed feedback decision-making process. It makes it an excellent candidate for producing solutions in a wide range of healthcare fields.
Patients in healthcare can benefit from policies learned through reinforcement learning algorithms. Without prior knowledge of the mathematical model of biological systems, reinforcement learning can develop effective solutions derived from previous observations. It makes this method more suitable in healthcare.
Reinforcement learning in dynamic treatment regimes is beneficial because it can make time-dependent judgments about the best therapy for a patient at a given point in time. By accounting for the delayed impacts of medicines, the application of reinforcement learning in healthcare can enhance long-term outcomes. Reinforcement learning helps in the synthesis of the best DTRs for chronic disorders.
Challenges in Using Reinforcement Learning
● There is a possibility of overloading of states if we don’t train it properly.
● RL is more suitable for complex problems, for simpler problems it’s an overkill.
● Computational power and maintenance requirement is high.
● Reward system calculation should be wise as it’s vital for the performance of the model.
Check out this article to understand the pros and cons of using reinforcement learning in detail.