2020 Study on Reinforcement Learning: Drivers, Restraints & Opportunities - ResearchAndMarkets.com

DUBLIN--(BUSINESS WIRE)--The "Reinforcement Learning: An Introduction to the Technology" report has been added to ResearchAndMarkets.com's offering.

This study was conducted in order to understand the current state of reinforcement learning and track its adoption along various verticals, and it seeks to put forth ways to fully exploit the benefits of this technology.

It will serve as a guide and benchmark for technology vendors, manufacturers of the hardware that supports AI, as well as the end-users who will finally use this technology. Decisionmakers will find the information useful in developing business strategies and in identifying areas for research and development.

The Report Includes:

• A general framework for deep Reinforcement Learning (RL) - also known as a semi-supervised learning model in the machine learning paradigm
• Assessing the breadth and depth of RL applications in real-world domains, including increased data efficiency and stability as well as multi-tasking
• Understanding of the RL algorithm from different aspects, and persuade the decision-makers and researchers to put more efforts into RL research

These days, machine learning (ML), which is a subset of computer science, is one of the most rapidly growing fields in the technology world. It is considered to be a core field for implementing artificial intelligence (AI) and data science. The adoption of data-intensive machine learning methods like reinforcement learning is playing a major role in decision-making across various industries such as healthcare, education, manufacturing, policing, financial modeling and marketing.

The growing demand for more complex machine working is driving the demand for learning-based methods in the ML field. Reinforcement learning also presents a unique opportunity to address the dynamic behavior of systems.

Key Topics Covered:

Chapter 1 Reinforcement Learning

• Reasons for Doing This Report
• Intended Audience
• Introduction to Reinforcement Learning
• Artificial Intelligence and Machine Learning
• Four Main Types of Machine Learning
• Reinforcement Learning vs. Supervised Learning vs. Unsupervised Learning
• Approaches to Reinforcement Learning Algorithms
• Characteristics of Reinforcement Learning
• Market Dynamics
• Drivers
• Restraints
• Opportunities
• Challenges of Reinforcement Learning
• Slower Interaction with Real Systems as Compared to Faster Simulated Environments
• Higher Variance and Instability
• Absence of Reproducibility Due to Lack of Standardized Benchmarks, Frameworks, and Evaluation Metrics
• Inappropriate Definition of Rewards, Actions, and States
• Lack of Generalization
• Future Aspects of Reinforcement Learning
• Future Applications of Reinforcement Learning Across Verticals
• Resources Management in Computer Clusters
• Traffic Light Control
• Robotics
• Web System Configuration
• Chemistry
• Personalized Recommendations
• Bidding and Advertising
• Games
• Market Potential
• Companies Working on Reinforcement Learning
• Analyst Credentials
• Related Reports

Chapter 2 Bibliography

Companies Mentioned

• Bonsai
• Deepmind Technologies
• Maluuba Inc.
• Mathworks
  

For more information about this report visit https://www.researchandmarkets.com/r/frue9p