Reinforcement Learning: An Introduction to the Technology

Reinforcement Learning: An Introduction to the Technology

Jan 2020 BCC Research ICT17 Pages Price :
$ 2500
Reasons for Doing This Report:

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 modelling and marketing. The growing demand for more complex machine working is driving the demand of learning-based methods in the ML field. Reinforcement learning also presents a unique opportunity to address the dynamic behavior of systems.
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. This study 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.
Table of Contents
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
BONSAI
DEEPMIND TECHNOLOGIES
MALUUBA INC.
MATHWORKS
Analyst Credentials
Related BCC Research Reports
Chapter 2 Bibliography

List of Table

List of Tables
Table 1 : Reinforcement Learning vs. Supervised Learning vs. Unsupervised Learning
Table 2 : Global Machine Learning Market, by Region, Through 2024

List of Chart

List of Figures
Figure 1 : Reinforcement Learning Process
Figure 2 : Reinforcement Learning Workflow
Figure 3 : Artificial Intelligence vs. Machine Learning vs. Reinforcement Learning
Figure 4 : Machine Learning Applications
Figure 5 : Types of Machine Learning
Figure 6 : Reinforcement Learning Market Dynamics
Figure 7 : Global Machine Learning Market, by Region, 2018-2024

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