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Towards Being Truly Intelligent: Next Wave of AI Technologies (Wave 2 - Reinforcement Learning)

  • ID: 5130135
  • Report
  • June 2020
  • Region: Global
  • 24 Pages
  • Frost & Sullivan
An Overview On Emerging Machine Learning/Artificial Intelligence Approach

As autonomy becomes the key objective of industries across the globe, artificial intelligence (AI) and machine learning (ML) systems too are being driven to adopt a more decision making role with an objective to find and implement the most effective methods of executing business goals. A key area of interest for businesses is to implement ML systems that can find avenues of improvement, which otherwise would be missed by manual analysis.

Reinforcement learning (RL) is a method of ML that focuses on finding the best possible behavior or method to achieve a predetermined set of objectives. These systems excel at discovering the best method to achieve predetermined goals.

In brief, this research service covers the following points:

  • Introduction to Reinforcement Learning
  • Applications of Reinforcement Learning
  • Innovators and Innovations
  • Growth Opportunities
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1.0 Executive Summary
1.1 Research Scope
1.2 Research Methodology

2.0 Reinforced Learning - Introduction
2.1 Reinforcement Learning Focuses on Finding and Executing the Best Possible Method for a Predefined Goal
2.2 RL Systems Revolves Around an Agent that Navigates in the Environment According to the State to Achieve Rewards
2.3 Model-free RL Methods Rely on a Trial and Error Method to Find the Most Efficient Approach Toward Goal Fulfilment
2.4 Model-based RL Constructs an Internal Model and Simulates an Action to Determine Outcome and Transitions Before Taking Action
2.5 Reinforcement Learning is a Computationally Intensive Method of Machine Learning and Thus Finds Limited Application at Edge

3.0 Innovations and Companies to Action
3.1 Multiple Research Studies and Deployments by Leading Companies and Universities Have Accelerated the Commercialization of RL
3.2 Robotics Has Been an Early Use Case for Reinforcement Learning Systems
3.3 Self-driving Cars Can Leverage RL to Take Complex Decisions in a Dynamic Environment
3.4 RL Systems are Being Used to Design Gameplays and to Enable Realistic Simulations in Virtual Environments
3.5 A Wide Range of Use Cases Based on RL are Being Developed Across Industries

4.0 Growth Opportunity
4.1 The Practical Applications of Theoretical Research in the Area of RL Have to be Explored by Industry-academia Collaboration
4.2 RL Systems Can Be Relied Upon To Understand the Complex Interplay Between Multiple Elements of an Environment

5.0 Industry Contacts
5.1 Key Contacts

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