Markdown Content - Reinforcement learning – applications and challenges

---
# Reinforcement learning – applications and challenges

**URL:** https://www.sigmoid.com/blogs/reinforcement-learning-part-2/
Date: 2019-12-13
Author: Sigmoid
Post Type: post
Summary: In the last blog we covered the basics of Reinforcement Learning (RL), its common rules and its terminologies. In this edition, we...Read More...
Categories: AI/ML
Tags: Cloud Transformation, Data Management
Featured Image: https://www.sigmoid.com/wp-content/uploads/2025/02/Reinforcement-Learning-–-Applications-and-Challenges-banner-opt.jpg
---

In the last [blog](/blogs/introduction-to-reinforcement-learning/) we covered the basics of Reinforcement Learning (RL), its common rules and its terminologies. In this edition, we will focus on its real-world applications, challenges associated and its relevance in the world of Data Science.

## Challenges in RL

As discussed in the last blog, an agent does an action in the environment based on the policy of the system. This policy is learned through the reward mechanism that the user defines. Now, defining a perfect reward mechanism is a fairly complex task. If the rewards are long-term, then the short-term actions of the agent whether right or wrong have a very small effect on the overall result. In order to optimize this whole ecosystem a lot of data is required. This is known as the Credit Assignment problem. This is one of the biggest challenges that researchers face while solving a problem using RL. However, researchers have come up with various short-term reward mechanisms and also a lot of custom optimizers which helps attain optima at a faster rate.

## Applications of RL

Despite having few challenges while defining the best reward mechanism, RL is the best choice of algorithm in high-dimensional control problems and various similar industrial applications.
Some of the applications of RL are:

###  Games

             The ability to play games is usually considered a consequence of intelligence, and Deep RL has been successfully applied to various range of games, and astonishingly enough with no information of dynamics of game as an input but the pixels. From playing perfect information board games such as go and chess to imperfect information games such as poker, RL has successfully beaten the experts in respective domains. It has also made its mark in various video games from classic Atari games to complex online games such as DOTA. Even in complex and multiplayer settings the AI can cooperate with human players to beat the opponents. However, it is important to note that RL can be very sample inefficient, for instance it took 45,000 years of [gameplay simulation](/blogs/reinforcement-learning-part-2/) for RL to learn to play DOTA.

### Recommendation System

             The ability of RL to deal with complex structure of information has made it a potential solution for recommendation systems. Alibaba, Taobao have successfully implemented RL on their e-commerce platform. Facebook is using RL to choose relevant notifications to send to its users. All this works with a simple and intuitive idea of reinforcing the content which is likely to generate better engagement from the user.

### Humanoid or Robots

             From navigation to performing automated motor tasks, RL has shown promising results at least in simulated environments. The development of robots with human like interaction is still an implementation which lies far ahead in the future. However, task-specific robots have successfully been built for relevant industrial applications. Future of RL in the industrial applications space looks promising. Microsoft recently acquired a startup working in deep RL for Industrial systems.

### Natural language Processing

              Reinforcement Learning has the ability to specialize in specific tasks which it learns by repeating it over and over, hence in the domain of NLP it has yielded significant results for goal-oriented chat bots. The aim of these chatbots is to answer a directed query for specific domain. For instance, it can help users book a ticket, find a reservation, and other areas such as machine translation, text generation etc.

### Finance

              Reinforcement Learning has the ability to specialize in specific tasks which it learns by repeating it over and over, hence in the domain of NLP it has yielded significant results for goal-oriented chat bots. The aim of these chatbots is to answer a directed query for specific domain. For instance, it can help users book a ticket, find a reservation, and other areas such as machine translation, text generation etc.

## Conclusion

Millions of data points are generated each second in the world of finance. Even Czars of financial industry find it difficult to predict trends consistently. Can an algorithm, with absolutely no knowledge of dynamics but just with data, come up with sequential dynamic strategy to choose relevant stocks? The answer is a definite yes. Scientific papers have been published on the profitable strategies in niche markets. RL can further be extended to areas such as option pricing, portfolio optimization and so on.

## About the Author

Abhijeet is a Data Scientist at Sigmoid. He mainly works in the domain of Recommendation Engines, Time Series Forecasting, Reinforcement Learning and Computer Vision.

    [lc_the_tags]

## Featured blogs

        [lc_get_posts post_type="post"
            posts_per_page="4" orderby="date" output_view="lc_get_posts_mycustom_view" output_number_of_columns="4"
            output_wrapper_class="row" output_article_class="shadow" output_hide_elements="Excerpt"
            output_excerpt_length="0" output_excerpt_text="Read More" output_heading_tag="span"
            output_featured_image_format="thumbnail" output_featured_image_class="card-img-left" ]

## Share

            [addtoany]

            Subscribe to get latest insights

## Talk to our experts

			Get the best ROI with Sigmoid’s services in data engineering and AI

## Suggested readings

					[View all](/blogs/)

						![](/wp-content/uploads/2025/02/Outsmarting-Humans-An-Introduction-to-Reinforcement-Learning-thumbnail-opt.jpg)

#### [Outsmarting Humans: An Introduction to Reinforcement Learning](/blogs/introduction-to-reinforcement-learning/)

						[Read blog](/blogs/introduction-to-reinforcement-learning/)

						![](/wp-content/uploads/2025/02/Data-Science-and-Cloud-–-The-future-of-analytics-thumbnail-opt.jpg)

#### [Data Science and Cloud – The future of analytics](/blogs/data-science-and-cloud-future-of-analytics/)

						[Read blog](/blogs/data-science-and-cloud-future-of-analytics/)

						![](/wp-content/uploads/2023/12/5-Best-Practices-for-Deploying-Machine-Learning-Models-thubmnail-opt.jpg)

#### [Real-time marketing measurement with multi-touch attribution for CPG](/blogs/real-time-marketing-measurement-with-multi-touch-attribution-for-cpg/)

						[Read blog](/blogs/real-time-marketing-measurement-with-multi-touch-attribution-for-cpg/)

---

## Categories

- AI/ML

---

## Navigation

- [WordPress.org](https://wordpress.org/)
- [Documentation](https://wordpress.org/documentation/)
- [Learn WordPress](https://learn.wordpress.org/)
- [Support](https://wordpress.org/support/forums/)
- [Feedback](https://wordpress.org/support/forum/requests-and-feedback)
- [Sigmoid](https://www.sigmoid.com/)
- [Community](https://community.wpmanageninja.com/portal/space/fluent-forms/home)
- [Docs](https://wpmanageninja.com/docs/fluent-form/)
- [Developer Docs](https://developers.fluentforms.com/)
- [Documentation](https://imagify.io/documentation/)
- [Rate Imagify on WordPress.org](https://wordpress.org/support/view/plugin-reviews/imagify?rate=5#postform)
- [Manage](admin.php?page=litespeed)
- [Settings](admin.php?page=litespeed-cache)
- [Image Optimization](admin.php?page=litespeed-img_optm)
- [Company](/about-sigmoid)
- [Newsroom](/newsroom)
- [Life at Sigmoid](/careers)
- [Takshashila](/takshashila)
- [Contact Us](/contact-us)
- [AI Strategy Blueprint your AI advantage](/enterprise-ai-strategy/)
- [Generative AI Drive innovation with Generative AI](/generative-ai/)
- [Responsible AI Build trust with ethical AI practices](/responsible-ai-in-enterprise/)
- [Agentic AI Reshape business with scalable agentic systems](/agentic-ai-solutions/)
- [AI Managed Services Ensure reliable AI performance](/ai-managed-services/)
- [Advanced Analytics Transform your business with data-driven insights](/advanced-data-analytics-solutions/)
- [Start Assessment](/agentic-ai-readiness-index/)
- [Data Strategy Strong data foundations for scalable AI](/data-analytics-strategy/)
- [Data Management Leverage data as a strategic asset](/ai-data-management-services/)
- [Data Ops Automate data for speed and quality](/data-devops/)
- [Data Engineering Deliver insights faster with scalable pipelines](/data-engineering/)
- [Cloud Transformation Modernize data to maximise efficiency](/cloud-migration/)
- [Download Whitepaper](/ebooks-whitepapers/building-data-products-in-a-data-mesh-to-drive-business-value/)
- [Data Modeling Structure data for better decisions](/data-modeling-services/)
- [Data Visualization Transform data into actionable stories](/data-visualization-service/)
- [BI Migration Enhance decision making with modern BI tools](/bi-migration/)
- [Data Observability Build trust with healthy, accurate data](/data-observability/)
- [Automated Insights Make smarter decisions with auto-generated insights](/automated-insights/)
- [Download Whitepaper](/ebooks-whitepapers/power-bi-hacks/)
- [CPG & Retail End-to-end analytics for planning, operations, and commercial excellence](/industries/cpg-analytics/)
- [Life Sciences Trusted intelligence across clinical, commercial, and operational workflows](/industries/life-sciences/)
- [Financial Services AI-powered analytics for risk, compliance and customer experience](/industries/banking-financial-analytics-services/)
- [Read case study](/case-studies/data-clean-room-enables-real-time-insights-to-improve-operational-efficiency/)
- [MediaIQ Advanced platform for in-flight marketing measurement](/accelerators/sigmoid-mediaiq-multi-touch-attribution-tool/)
- [CampaignIQ AI-driven platform for optimized campaign budget allocation](/accelerators/sigmoid-campaigniq/)
- [AssistBot GenAI email assistant that automates human-like responses](/accelerators/sigmoid-assistbot-for-ai-email-assistant/)
- [CreativeBot GenAI tool for personalized and brand-aligned creative design](/accelerators/sigmoid-creativebot/)
- [SocialBot GenAI platform to analyze digital conversations and trends](/accelerators/#marketing|socialbot)
- [DemandIQ Predict trends accurately and optimize inventory management](/accelerators/sigmoid-demandiq/)
- [NetworkIQ Track and optimize logistics operations in real-time to quickly address disruptions](/accelerators/sigmoid-networkiq/)
- [SupplyIQ End-to-end platform to optimize supply chain operations](/accelerators/sigmoid-supplyiq/)
- [ProcurementIQ Automated procurement operations for maximum savings, compliance and efficiency](/accelerators/sigmoid-procurementiq/)
- [RapidML Accelerated deployment for machine learning models](/accelerators/sigmoid-rapidml/)
- [DataGuard Comprehensive platform for proactive data quality management](/accelerators/data-quality-tool-sigmoid-dataguard/)
- [CloudPulse Cloud cost optimization platform with multi-cloud management](/accelerators/sigmoid-cloudpulse/)
- [RAPID GenAI foundation with built-in governance and cost clarity](/accelerators/sigmoid-rapid/)
- [AnalyticsBot GenAI based platform to streamline decision-making in analytics](/accelerators/sigmoid-analyticsbot/)
- [DataConnect Seamlessly ingest, integrate and harmonize data from diverse sources](/accelerators/sigmoid-dataconnect/)
- [Reconica AI-powered data harmonization and reconciliation engine](/accelerators/sigmoid-reconica/)
- [ConverseBot GenAI driven insights generation for automated insights from reports](/accelerators/#sales|conversebot)
- [iNRM Cross-lever revenue growth optimization platform](/accelerators/sigmoid-inrm/)
- [AssortmentIQ Optimize shelf layouts and assortment mix at scale with AI-based insights](/accelerators/sigmoid-assortmentiq/)
- [Read Whitepaper](/ebooks-whitepapers/building-agentic-ai-chatbots-for-business-process-transformation/)
- [Listen Podcast](/events/podcast/how-jack-in-the-box-is-redefining-personalization-and-supply-chain-with-ai/)
- [Blogs](/blogs/)
- [White Papers](/ebooks-whitepapers/)
- [Case Studies](/case-studies/)
- [Podcast](/events/podcast/#Podcasts)
- [Read Blog](/blogs/the-genai-adoption-triad-responsibility-ethics-and-explainability/)
- [ConverseBot](/accelerators/#sales|conversebot/)

## Tags

- Cloud Transformation
- Data Management

---

## Footer Links

- [Talk to our AI experts](/contact-us/)
- [AI Strategy](/enterprise-ai-strategy/)
- [Agentic AI](/agentic-ai-solutions/)
- [Generative AI](/generative-ai/)
- [AI Managed Services](/ai-managed-services/)
- [Responsible AI](/responsible-ai-in-enterprise/)
- [Advanced Analytics](/advanced-data-analytics-solutions/)
- [Data Strategy](/data-analytics-strategy//)
- [Data Engineering](/data-engineering/)
- [Data Management](/ai-data-management-services/)
- [Cloud Transformation](/cloud-transformation/)
- [Data Ops](/data-devops/)
- [Data Visualization](/data-visualization-service/)
- [Automated Insights](/automated-insights/)
- [BI Migration](/bi-migration/)
- [Data Modeling](/data-modeling-services/)
- [Data Observability](/data-observability/)
- [CPG & Retail](/industries/cpg-analytics/)
- [Financial Services](/industries/banking-financial-analytics-services/)
- [Life Sciences](/industries/life-sciences/)
- [Case Studies](/case-studies/)
- [Thought Leadership](/ebooks-whitepapers/)
- [Blogs](/blogs/)
- [Company](/about-sigmoid/)
- [Newsroom](/newsroom/)
- [Accelerators](/accelerators/)
- [Careers](/careers/)
- [Privacy Policy |](/privacy-policy/)
- [Cookie Policy](/cookie-policy/)