Trending December 2023 # Airflow For Orchestrating Rest Api Applications # Suggested January 2024 # Top 21 Popular

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This article was published as a part of the Data Science Blogathon.

“Apache Airflow is the most widely-adopted, open-source workflow management platform for data engineering pipelines. It started at Airbnb in October 2014 as a solution to manage the company’s increasingly complex workflows. Most organizations today with complex data pipelines to be managed leverage Apache Airflow to schedule, sequence, monitor the workflows.”

Airflow provides an easy-to-use, intuitive workflow system where you can declaratively define the sequencing of tasks (also known as


or Directed Acyclic Graph). The Airflow workflow scheduler works out the magic and takes care of scheduling, triggering, and retrying the tasks in the correct order.  

“Start Task4 only after Task1, Task2, and Task3 have been completed….”

“Retry Task2 upto 3 times with an interval of 1 minute if it fails…”

“Which task took the longest in the workflow? …”

“What time did taskN take today vs one week back? …”

“Email the team when a critical task fails…”

The Use Case for Airflow

So, where does a workflow management system fit? And how do you know you need to use it? Let’s say you are working for the IT division of a health care organization, and you need to run some analytics on patient records that you receive from a vendor hospital. You have developed that awesome Apache Spark-based application, which is working like a charm. You need that application run daily against the data that comes in from the hospital. A further requirement is that the output of that analysis needs to be pushed as input to a time-critical downstream application which determines the composition and quantity of factory production units for a test medicine for that day.

Initially, a simple cron job or a Jenkins-based job might suffice until things get bigger. Let’s say two more upstream hospitals get added to the fray. One pushes data to an S3 bucket; another gives a REST API-based interface from which you need to fetch data, and yet another in-house system dumps data to a database. You need to now run your analytics application against the data from all these upstream systems before running the downstream app. This is where the beauty of Airflow comes into play.

Airflow as a mainstream DevOps tool has been widely adopted since it was launched eight years ago to orchestrate BigData and ETL pipelines. As your systems and processes become bigger, managing the scalability and monitoring using custom scripts or cron-based solutions becomes difficult—this is where it fits in.

Airflow UI

times the task started/ended.

The Tree View UI shows you the historical runs broken down by tasks – this is most useful when you want to compare performance between historical runs.

REST API with Python Operators

There are  several operators and provider packages that Apache Airflow supports. Depending on your use case, you get to pick and choose what is most suitable. When I started learning Airflow, what I found most helpful and flexible were the Python-based operators. My applications were running in less than 24 hours with the combination of PythonOperator and PythonSensor 

With these two, you should be able to fit in the general use case described above. All you need is basic Python knowledge!

Structure of an A G

1. First come the imports:

2. Then comes the definition of the DAG constructor/initialization.

Here’s where you give the name of the workflow process that you want to see in the UI, the default retries for tasks, etc

dag = DAG( 'patient_data_analysis', default_args={'retries': 1}, start_date=datetime(2023, 1, 1), catchup=False, ) dag.doc_md = __doc__ ## Operators start = DummyOperator(task_id='start', dag=dag) op1 = PythonOperator( task_id='watch_for_data_dump_on_s3_bucket_pushed_byx_upstream_application_1', python_callable= _placeholder_function1, dag=dag) op2 = PythonOperator( task_id='fetch_data_from_upstream_REST_application2_and_dump_to_s3', python_callable= _placeholder_function2, dag=dag) op3 = PythonOperator( task_id='fetch_data_from_upstream_cloudant_application3_and_dump_to_s3', python_callable= _placeholder_function3, dag=dag) op4 = PythonOperator( task_id='run_analysis_on_all_patient_data_on_s3_dumps', python_callable= _placeholder_function4, dag=dag) determine_production_dosage = BranchPythonOperator( task_id='determine_production_dosage', python_callable=_determine_production_dosage, dag=dag ) production_path_1 = PythonOperator( task_id='production_path_1', python_callable= _placeholder_function5, dag=dag) production_path_2 = PythonOperator( task_id='production_path_2', python_callable= _placeholder_function6, dag=dag) end = DummyOperator(task_id='end',trigger_rule='one_success', dag=dag)

Here is where we have the breakdown of tasks in the flow. We have used three kinds of Operators.

PythonOperator –  which calls the Python callable or function which contains the actual task processing logic

BranchPythonOperator  – which is useful when you want the workflow to take different paths based on some conditional logic.

DummyOperator – which is a convenience operator to try out some POC flow quickly or in this case- gives a structure to the flow  – start and end.

Note that all the operators are connected using the same “dag” object reference.

4. Sequence your tasks

## Flow

The dependencies between your tasks can be declared using this intuitive flow notation.

The start operator will kick off three tasks in parallel – op1, op2, op3

Only when op1, op2, and op3 are done the op4 task will get started

The determine_production_dosage can result in either of the paths production_path_1 or production_path_2

And finally, execution of either path results in the end.

In this case, I have just given placeholder functions. We’ll get into what it should hold in the next section.  Special mention to the _determine_production_dosage(). This is the function called by the branch operator. As the code illustrates, this function’s return value is the operator’s name in the workflow.

PythonOperator and PythonSensor Combo

The following working code covers the following concepts.

How to use the  PythonOperator and callable to make REST API calls to generate a Bearer Token

And use that Bearer Token in subsequent API calls that call some business logic (in this case, it is calling a Spark application on a cloud provider API)

Concept of passing data between tasks using xcom

How to use PythonSensor operator to poll/wait for asynchronous task completion

How to dynamically construct the REST API endpoint based on the value returned from a previous task ( NOTE: This is one use case where I found the power and simplicity of PythonOperator come into play. I had initially tried the SimpleHttpOperator – but found the PythonOperator to be more flexible! )

Source code for

## Import statements and DAG definition

import json import requests from datetime import datetime from airflow import DAG from airflow.operators.python import PythonOperator from airflow.sensors.python import PythonSensor dag = DAG( 'serverless_spark_pipeline', default_args={'retries': 1}, start_date=datetime(2023, 1, 1), catchup=False, ) dag.doc_md = __doc__

## Python callable for getting a Bearer Token

api_key='CHANGEME' def _get_iam_token(ti): headers={"Authorization": "Basic Yng6Yng=", "Content-Type": "application/x-www-form-urlencoded"} data="grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey="+api_key res =,headers=headers,data=data) ## Push the token using key, value ti.xcom_push(key='access_token', value= access_token)

## Python Operator for getting the Bearer Token; It calls the Python callable _get_iam_token

generate_iam_token = PythonOperator( task_id = 'get_iam_token', python_callable= _get_iam_token, dag=dag)

## Python callable for calling a REST API

instance_id='CHANGEME' def _submit_spark_application(ti): # Pull the bearer token and use it to submit to REST API access_token=ti.xcom_pull(key='access_token') headers = {"Authorization": "Bearer " + access_token, "Content-type": "application/json"} finalurl = url+instance_id+'/spark_applications' data=json.dumps({"application_details": {"application": "/opt/ibm/spark/examples/src/main/python/", "arguments": ["/opt/ibm/spark/examples/src/main/resources/people.txt"]}}) res =,headers=headers,data=data) # Push the application id - to be used on a downstream task ti.xcom_push(key='application_id', value= application_id)

## Python Operator for submitting the Spark Application; It calls the Python callable _submit_spark_application

submit_spark_application = PythonOperator( task_id = 'submit_spark_application', python_callable= _submit_spark_application, dag=dag) def _track_application(ti): # Pull the application id from an upstream task and use it.. application_id=ti.xcom_pull(key='application_id') access_token=ti.xcom_pull(key='access_token') headers = {'Authorization': 'Bearer ' + access_token} # Construct the REST API endpoint dynamically based on the data # from a previous API call finalurl = ae_url+instance_id+'/spark_applications/'+application_id+'/state' res = requests.get(finalurl,headers=headers) # Keep polling the REST API to check state of application submission until a # terminal state is reached if state == 'finished' or state == 'failed': # Push the value of state as xcom key, value pair. # It can be later used for example in a BranchPythonOperator t1.xcom_push(key='state',value=state) return True else: return False

## Python Sensor for tracking a REST APU. It calls the Python callable _track_application

  track_application = PythonSensor( task_id = 'track_application', python_callable= _track_application, dag=dag)

## Operator flow

This example is based on a REST API call to a cloud provider API that submits a spark application, gets the application ID, and keeps polling for the application’s state based on that application ID. And finally, when the application either finishes or fails, it ends the workflow execution.

The Python callable functions make use of the standard requests module. In the example above, POST and GET. You can use the same approach for other REST API calls, PATCH, PUT, DELETE, etc.

End Notes

Here’s a snapshot of the main DAG UI page. If you are starting Airflow, here are some newbie tips.

You need to toggle and enable the DAG to make it active and execute automatically through the tasks.

Also, be aware that whenever you make a change in the DAG file, it takes about 1 minute to refresh your code and reflect it in the DAG Code tab in the UI.  (The DAG files, which are nothing but python files, are located in the airflow/dags folder of your installation)

This article showed you how to get quickly started with

A simple working DAG that you can get it up and running by defining the sequencing of tasks

Introduction to Python-based operators and sensors that can be easily adapted to call any backend REST API services/applications

How to orchestrate various asynchronous REST API services by polling and passing the relevant data between tasks for further processing

Depending on the use case, your tasks’ data source and data sink. You will need to evaluate what Airflow operators are suitable. Many tuning knobs for airflow can be further configured as you get deeper

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


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Three Useful Creative Writing Applications For Linux Users

If you are a writer, you would have used tools like Final Draft or Scrivener to create your work. What if you are a Linux user and those tools are not available on Linux platform? What choices do you have to create your novels, scripts, or screenplays. Well, you are not totally lost. Here are some Linux-based creative writing applications for you.

1. Celtx

One of the main features of Celtx includes index cards you can use to brainstorm and organize your thoughts by plotline, the ability to arrange scenes into chapters and parts. There are two versions of the application available: a free version, and a premium version that provides extra features such as the ability to arrange index cards into plot and timeline views (they are displayed in a flat list only in the free version).

2. Plume

In contrast, Plume Creator is focused on prose-based creative writing. It also maintains your writing in scenes and chapters (with early support for an outliner), but in addition to characters also helps manage places and items, which are key features for writers of fiction. Plume lets you maintain a collection of these elements and associate them with your work(s) as appropriate for easy reference in the right-hand panel.

Plume also features a fullscreen (i.e. distraction-free) interface, and the ability to attach a synopsis and notes individually to the novel as a whole, or at the chapter or scene level. It is available for download as a .deb file.

3. Storybook

Don’t let tools like the Final Draft and Scrivener fool you… Mac and Windows aren’t the only creative writing platform in town (although Scrivener has been testing a Linux beta for some time now). If you’re a Linux user with an idea for a novel, you’ve got access to all the tools you need.

Let us know if you are using other writing tools not mentioned in the above list.

Image credit: write

Aaron Peters

Aaron is an interactive business analyst, information architect, and project manager who has been using Linux since the days of Caldera. A KDE and Android fanboy, he’ll sit down and install anything at any time, just to see if he can make it work. He has a special interest in integration of Linux desktops with other systems, such as Android, small business applications and webapps, and even paper.

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Wix Integrates Google Url Inspection Api

Wix is integrating Google’s URL Inspection API directly into the new Wix Site Inspection Tool, providing users with an easy way to achieve and maintain a good search presence.

Users can now view insights into how Google is indexing a site and debug technical issues like structured data on a sitewide basis.

Search Console URL Inspection API

In January 2023, Google announced a new Application Programming Interface (API) that enables the creation of tools that can access URL inspection data.

This is called the Search Console URL Inspection API.

The launch of this API was important because it allowed users to create custom ways to access and use valuable data on how a site is performing in Google.

Google’s January announcement of the API explained why it is important:

“The Search Console APIs are a way to access data outside of Search Console, through external applications and products.

With the new URL Inspection API, we’re providing a new tool for developers to debug and optimize their pages.

You can request the data Search Console has about the indexed version of a URL; the API will return the indexed information currently available in the URL Inspection tool.”

Wix Integration of RL Inspection API

Users of Wix now have access to this data within a Wix dashboard.

Nati Elimelech, Head of SEO at Wix, explained what Wix users would be able to do:

“The tool will report any index status that Google is reporting, sometimes with easier language to understand.

For example, it could be “crawled but not indexed” or “blocked by robots.txt” etc. Each index status detail will have a “learn more” link to an article explaining it and mentioning the common reasons why it could happen, with links to more relevant explanations.

We also tell the users exactly what app and type of page each URL is so they can easily know where they need to take action.”

Google’s API also helps debug structured data, which is super important. Proper structured data makes websites eligible to have their webpages displayed in what Google terms enhancements to search.

Enhancements to search are a special kind of search result that stands out. They are also known as Rich Results.

Google can sometimes read the information on a page and algorithmically decide to include it as a rich result.

But using structured data makes it easier for Google to do.

Consequently, it’s vital to use structured data to become eligible for enhanced search results that make a webpage result stand out and attract more site visitors.

Google’s structured data tool uses words like “warning,” which gives the false impression that something is wrong. But that’s not always the case. Google marks potential issues with a “warning” for an optional variable.

Wix’s implementation of the structured data debugging put the warnings into the proper context by naming it an “optional fix.”

Elimelech confirmed that Wix’s implementation of the API would help debug structured data:

“Absolutely, if a required field is missing, it will show as “issue” in the table and in the panel you will see which field is missing.

Users will see the exact fields that are missing in the structured data markup and its severity – including issues that require action, or just optional fields that can be added to enrich the markup.

If it’s a recommended field that is missing you will see “optional fix” in the table and in the panel of the exact fields that are missing.

We adjusted some of the language to better reflect the actual impact on the users’ site, so we changed what may feel like alarming warnings for recommended fields to “optional fix” so users will understand that it is a recommendation and not something that is making their pages ineligible for rich results on Google’s SERPs.”

Solving Indexing Issues

The new Wix URL Inspection tool will also be able to call attention to indexing issues, which will give businesses an early warning to fix something before it becomes a major problem.

According to Wix:

“With this tool, users can debug any issue that they would get if they manually used Search Console’s inspect URL tool.

Users can understand the index status of their pages in Google and the specific reason for it.

For each issue, we have additional resources to help guide users.

These articles provide in depth explanations about each response, the possible reasons for it and how to fix it.

Users will also be able to debug their mobile readiness and their structure data status.”

Wix Platform Handles Technology

The value of Wix is that it enables businesses to focus on doing business without having to deal with the underlying technology.

Wix continues to be a leader in professional website builders by being the first SaaS site builder platform to integrate the Search Console URL Inspection API and making it easy to interpret the data and use it to help maximize search presence.

Citation Read the Wix Documentation:

Get to know the Wix Site Inspection tool

Featured image by Shutterstock/Gorodenkoff

How Can Blockchain Applications Save Money?

Financial transactions and trading operations are critical components of a global or national economy. The global economic system manages trillions of dollars in transactions while servicing billions of clients. Because beneficiary intermediaries are involved in the transactional process, both parties must pay a transaction fee.

Exploring new technologies is motivated by the need to do activities more quickly and inexpensively. Furthermore, the present digitalization trend is forcing organizations to keep up and integrate with the latest developments.

Elimination of third parties and related expenses

The basic goal of blockchain technology development was to eliminate third parties or intermediaries. Lawyers, banks, brokers, and the government were all engaged in the traditional system of two groups negotiating a real estate sale, which took more time and cost more money.

The emergence of blockchain technology allowed the two parties to conduct the transaction directly. As a result, the dealing parties’ direct engagement reduced operating expenses and time consumption. Blockchain is also very secure because of its cryptographic, time-stamped, and tamper-proof properties. Anyone allowed on the blockchain network may validate transaction details from anywhere.

Crowd funding

Crowdfunding is the process of obtaining funds for a project from a group of people, with each person contributing a small amount over the internet. Decentralized financing using STOs (Security Token Offerings), ICOs (Initial Coin Offerings), and now IEOs (Initial Exchange Offerings) has allowed companies to circumvent some investors. Furthermore, obtaining funds for companies using new methods like Blockchain is faster and less expensive, whereas traditional fundraising takes a long time and costs a lot of money.

Minimization of operational expenses

By lowering transaction costs, using a blockchain system can help businesses reduce overhead expenditures. Payments in cryptocurrency are made on a decentralized system controlled by peer-to-peer networks, removing the need for centralized authentication.

This enables businesses to accept bitcoin payments while incurring lower transaction fees. Because of the peer-to-peer cryptographic system, businesses can now transact globally instantly and economically without any barriers.

Furthermore, using smart contracts to automate transactions reduces the likelihood of a claim being filed in the event of a contract breach. In addition, Blockchain improves supply chain efficiency by making product monitoring more systematic and transparent.

Customization of technology Improved Compliance

Several enterprises require digital identification technologies and KYC (know your customer) to establish simple identity verification systems. This aids in achieving more transparency, optimizing data access, and controlling IT expenses while adhering to certification criteria. Blockchain has an encrypted data storage function that keeps data safe and unchangeable.

Switching to a ledger-based ID system protects the network from hacking and decrypting user identities. Protecting operational and customer data is critical for organizations since any breach can have a negative influence on the company’s reputation.

Building Costs with Blockchain Applications

The expenses of developing blockchain applications are influenced by a variety of factors like −

Solution costs, development costs

Costs of relocation, training, and onboarding

Costs of storage and power

Costs of development

The growing popularity of blockchain technology and its uses has resulted in an increase in demand for blockchain developers. Blockchain developers are ranked first on LinkedIn’s list of the top five emerging careers in the United States for 2023. This demonstrates that the organizations are committed to creating blockchain applications. Professional blockchain developers are required for the development of blockchain applications. The cost of hiring a blockchain developer is determined by the developer’s location, abilities, level of expertise, and project scope.

To design mobile/web apps that give precise blockchain logic, the developer must also be knowledgeable with web frameworks and programming languages such as Golang, NodeJS, Javascript, and Ruby on Nails. The developer must also be familiar with the working culture of blockchain frameworks like Hyperledger, R3 Corda, Ethereum, and Multichain. The programmer should have a fundamental understanding of blockchain programming languages such as Solidity, Sophia, Serpent, and Vyper.

Solution Costs

A decentralized app (DApp) or an enterprise blockchain are the two choices for deploying blockchain technology in an organization. DApps built on public blockchain platforms like Ethereum are typically chosen by new firms with a blockchain use case concept. Enterprise blockchain is used by established firms to improve productivity, reduce costs, and optimize their business technology.

The majority of enterprise blockchain networks are secure and private. Establishing a DApp, on the other hand, is far less expensive than developing a corporate blockchain.

Costs of migration, training, and onboarding

When altering the complete functional system and transferring the database from a centralized to a decentralized design, a significant amount of money is spent. New resources, ranging from hardware to software and specialists, must be allocated. The whole firm’s employees, not just the IT team, would require adequate onboarding. The company’s employees are trained to ensure that they are aware of and comfortable with the new resources and structure. All of these variables increase the cost.

Costs of storage and power

Adopting Blockchain might result in high power expenditures, depending on the consent algorithms used. Consent algorithms such as PoW (Proof of Work) use processing power to verify the legitimacy of data destined for the Blockchain. Mitigation will necessitate the use of alternate consent algorithms and specialized mining equipment. Data redundancy has an impact on the cost of data storage. The increased data load slows down the system, necessitating more storage and raising operational costs.

Banking Fees are Reduced

Some believe that blockchain technology will eventually abolish the necessity for public banking. It can certainly eliminate or drastically cut the amount of money your company spends on banking fees.

When financial transactions are completed without the use of a centralized server or bank, you are effectively bypassing their services. Instead of paying monthly transactions or banking fees, you may invest that money in your company.

Over the course of your company’s fiscal year, not having to utilize a bank to convert money might save you hundreds, if not thousands, of dollars.

Reduces Taxes

Blockchain technology can help you reduce your company’s tax obligations. You could elect to accept cryptocurrencies as a method of payment, for example, through blockchain transactions.

Blockchain can help you save time and money in your business.

Transactions on the Blockchain do not take as long as transactions on credit cards. Whereas credit card transactions might take several minutes, they are almost instantaneous.

Your computer, cash register, or credit card machine no longer has to exchange information with a bank since blockchain information is not held at a financial institution. Before swaps may be allowed and finalized, there is no back-and-forth between your system and a bank’s.

Because of the time saved, your company will be more productive and spend less money.

How To Get And Use A Google Maps Api Key For Your Business

The page types typically include a map to show users where the location is, what are the cross streets, and any major landmarks. This enables the user to easily find the location and increases a brand’s opportunities for conversions.

With numerous mapping options available, it’s crucial to familiarize yourself with them and understand how to implement them — especially the top contender: Google Maps.

And for that, you’re going to need a Google Maps API Key.

In this column, you’ll learn from my colleague/co-author Marshall Nyman and I what a Google Maps API Key is and how to get started.

What Is a Google Maps API Key?

Google offers Maps, Places, and Routes to facilitate “real-world insights and immersive location experiences” to consumers on your website or app.

Maps allow you to share static or dynamic maps, Street View imagery, and 360° views of your location with your customers.

Places enable searchers to find specific places using phone numbers, addresses, and real-time signals.

Routes enable you to give your users the best way to get to your location with high-quality, easy-to-navigate directions and real-time traffic updates.

A Google Maps API Key allows you to integrate these mapping technologies into your website.

Getting Started with Google Maps

To get started, log into Google Cloud Platform or create an account if you do not have one already.

Some mapping technology has associated costs and so you will need to set up billing by adding a credit card on file.

For new Google Cloud accounts, you will receive $300 in free credits for setting up billing. You can set up billing information here.

Visit the pricing page to get an idea of what the API Key usage will cost. Google provides up to $200 a month in free usage.

Anything over $200 will be charged to the card on file.

How to Create API Keys

To create an API Key, you will navigate to APIs & Service, then choose Credentials in the dropdown. It will take you to this page where you can create the API key.

Once created, adding some restrictions to your key is recommended.

Google has a list of best practices for adding restrictions to keep your keys safe and secure, such as deleting API keys that are no longer needed and using caution when regenerating keys.

Adding the API Key to Your Requests

Once the key has been created and restrictions are added, you are ready to place it on your site.

An API key is required for every Maps JavaScript API request and should be loaded via a script tag.

This may be added to either your HTML file, or added dynamically to a separate JavaScript file.

Google recommends reviewing both approaches so you can choose what is appropriate based on how your code is structured.

The code needed is:

And you replace YOUR_API_KEY with the API key you created.

Visit Google’s API Key errors documentation to resolve any issues or errors that come up during setup.

Static Versus Dynamic Maps

You have the ability to add two different types of maps to your pages: static and dynamic maps.

Static maps just display the map as an image. You are not able to zoom or adjust the map, but the cost is much lower.

Dynamic maps, on the other hand, are not only interactive but customizable, as well. Dynamic maps also have the ability to have a branded logo pin.

The cost difference between the two map types can be significant, with dynamic maps at $7 per 1,000 requests versus $2 per 1,000 for static maps.

If your pages generate a lot of traffic, this could be a significant cost.

If your costs are currently very high from dynamic maps, it might be worth considering a static map instead.

Places and Routes

Implementing the Google Maps API Key on your site means there are other features available to you, as well, such as Places and Routes. Both of these can improve your customer’s experience.

To get started with developing the Directions API, set up your Google Cloud project. Then, review this list of all parameters when building your Directions API HTTP request.

The following place requests are available for your business, according to Google:

Place Search: Returns a list of places based on the user’s location or search string.

Place Details: Returns more detailed information about the location, including reviews.

Place Photos: provides access to millions of place-related photos stored in Google’s Place database.

Place Autocomplete: Automatically fills in the name and/or address of a place as a user types.

Query Autocomplete: Provides a query prediction service for text-based geographic searches, returning suggested queries as users type.

For pricing information for the Places API and Place Autocomplete service, view the Places API Usage and Billing documentation here.

Google is continuously enhancing its technology to evolve and adapt to the ever-changing needs and expectations of local consumers.

As more consumers turn to search to find products and services near them, it’s crucial for businesses across all verticals to provide an optimal user experience to stand out from competitors and build a positive brand experience.

This starts with optimizing your local listings everywhere your business can be found and extends to your Maps presence.

Driving customers to your storefront in the easiest, most direct route possible while providing all the information they need to get there is essential to win customer loyalty.

Remember, the maps used on the sites are available from several mapping providers.

Google has been the main player in the space but Bing, Apple, and now even Amazon are looking to provide mapping options for brands.

More Resources:

Reinforcement Learning: Benefits & Applications In 2023

Machine learning algorithms are used in a wide range of applications, from image recognition to natural language processing (NLP) and predictive analytics. One major challenge in the field of machine learning is designing algorithms that can learn to make complex, long-term decisions in dynamic environments. This is particularly relevant in fields such as robotics and autonomous systems, where the ability to adapt to changing circumstances is crucial.

Reinforcement learning is a type of machine learning algorithm that focuses on training models to make decisions in an environment in order to maximize a reward. This is typically done through trial and error, as the algorithm receives feedback in the form of rewards or punishments for its actions.

In this article, we’ll explore what reinforcement learning is, how it works, its applications, and its challenges.

What is reinforcement learning (RL)?

Reward rules are determined in the reinforcement learning algorithms. The agent of the model tries to reach maximum rewards through its actions. The algorithm starts with trials and learns to make decisions by itself to gain maximum rewards.

Reinforcement learning models can gain experience and feedback (rewards) from their actions which help them to improve their results. This machine learning approach can be best explained with computer games.

What is the level of interest in reinforcement learning?

Reinforcement learning may be a key player in further development and the future of AI. So, the interest in reinforcement learning has been continuing for the last five years. The machine learning domain has been improving reinforcement learning models with new areas such as deep reinforcement learning, associative reinforcement learning, and inverse reinforcement learning. The interest in reinforcement learning is seen below from the chart.

Source: Google Trends

How does it work?

There are five key elements of reinforcement learning models:

Agent: The algorithm/function in the model that performs the requested task.

Environments: The world in which the agent carries out its actions. It uses the current states and actions of the agent as input, rewards, and the next states of the agents as output.

States: It refers to the situation of the agent in an environment. There are current and future/next states.

Actions: The moves are chosen and performed by the agent to gain rewards.

Rewards: Reward means desired behaviors that are expected from the agent. Rewards are also called feedback for the agent’s actions in a given state and are described as results, outputs, or prizes in the model.

Different algorithms and approaches are used in the reinforcement learning models. Some of them are listed below.

Markov Decision Processes (MDPs): It is a framework that is used to model decision-making processes. The decision maker, the states, actions, and rewards are the key elements of MDPs. MDPs are effective for formulating reinforcement learning problems.

SARSA (State-Action-Reward-State-Action): It is an algorithm to learn a Markov decision process policy. The agent in its current state selects and performs an action and gains a reward for its action. Then, the agent gets into a new state and selects a new action.

Q-learning: It is a reinforcement learning algorithm. It does not need a model to learn the value of the actions and there is no policy. It means that it is a self-directed model.

Deep Reinforcement Learning: Reinforcement learning models are used with artificial neural networks to solve high-dimensional and complex problems. Deep reinforcement learning algorithms can work with large datasets. DeepMind’s game, AlphaGo Zero is a popular example for deep reinforcement learning.

There is a simple flow for the agent–environment interaction in a Markov decision process below.

What are the applications of reinforcement learning?

A large amount of data is required for reinforcement learning models. That means it is not applied in the areas which have limited data, but it may be ideal for robotics and industrial automation and building computer games. Reinforcement learning algorithms have the ability to make sequential decisions and learn from their experience. That is their distinguishing feature from traditional machine learning models. Common areas where reinforcement learning is used are listed below:

Computer Games: Pac-Man is a well-known and simple example. Pac-Man’s (the agent of the model) goal is to eat the food in the grid (the environment of the model), but not get killed by the ghost. Pac-Man is rewarded when it eats food and loses the game when it is killed.

Industrial Automation and Robotics: Reinforcement learning helps industrial applications and robotics to gain the skills themselves for performing their tasks.

Traffic Control Systems: Reinforcement learning is used for real-time decision-making and optimization for traffic control activities. There are existing projects such as the project to support air traffic control systems.

Resources Management Systems: Reinforcement learning is used to distribute limited resources to the activities and to reach the goal of resource usage.

Advertising: Reinforcement learning supports businesses and marketers to create personalized content and recommendations.

Other: Reinforcement learning models are also used for other machine learning fields like text summarization, chatbots, self-driving cars, online stock trading, auctions, and bidding.

What are the challenges of reinforcement learning?

Reinforcement learning is not a new area in machine learning and progress is still continuing despite the challenges. Those challenges are summarized below:

Reinforcement learning needs large datasets to make better benchmarks and decisions.

When the model’s complexity increases, reinforcement learning algorithms need more data to improve their decisions. That means the environments of the model may become more difficult to create a reinforcement learning model.

The results of reinforcement learning models depend on the agent’s exploration of the environment and it brings limitations to the model. The agent takes action according to the environment and its current state. If the environment changes constantly, making a good decision could be difficult.

The design of the reward structure of the model is another challenge for reinforcement learning. The agent uses rewards and penalties to make a decision and perform its task. The way the agent is trained in the model is the key to the success.

For more on different types of machine learning approaches, feel free to read our other articles:

If you have questions about reinforcement learning, we would like to help:

This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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