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市場調查報告書

成為真正的智能:人工智能技術的下一波(第二波:強化學習)

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

出版商 Frost & Sullivan 商品編碼 950636
出版日期 內容資訊 英文 24 Pages
商品交期: 最快1-2個工作天內
價格
成為真正的智能:人工智能技術的下一波(第二波:強化學習) Towards Being Truly Intelligent: Next Wave of AI Technologies (Wave 2 - Reinforcement Learning)
出版日期: 2020年06月28日內容資訊: 英文 24 Pages
簡介
隨著自治已成為全球行業的主要目標,人工智能(AI)和機器學習(ML)系統還需要找到並實施最有效的方法來執行業務目標。針對更多的決策角色。公司關注的關鍵領域是實施ML系統,該系統可以找到改善手動分析遺漏的方法。強化學習(RL)是ML的一種方法,著重於尋找可能的最佳行為或方法來實現一組預定義的目標。這些系統擅長尋找實現給定目標的最佳方法。

本報告涵蓋了強化學習(RL),並對其引入,應用,創新者以及創新和增長機會進行了詳細分析。

第1章執行摘要

  • 調查範圍
  • 調查方法

第2章強化學習:簡介

  • 強化學習側重於尋找和執行針對預定目標的最佳方法
  • RL Systems圍繞著在基於狀態的環境中移動以獲得獎勵的代理商展開工作
  • 無模型RL方法使用反複試驗方法來找到實現目標的最有效方法
  • 基於模型的RL建立內部模型,模擬動作,並在執行動作之前確定結果和過渡
  • 強化學習是機器學習的一種計算密集型方法,可以在邊緣發現有限的應用程序。

第3章創新/C2A(建議採取的行動)

  • 大型公司和大學的大量研發加速了RL的商業化
  • 機器人技術是強化學習系統的早期使用案例
  • 自動駕駛汽車可以在動態環境中使用RL做出複雜的決定
  • RL系統用於虛擬環境中的遊戲設計和逼真的模擬
  • 基於整個行業開發的基於RL的廣泛用例

第4章增長機會

  • RL領域理論研究的實際應用需要通過產學合作研究
  • RL系統對於理解環境的多個要素之間的複雜相互作用是可靠的

第5章行業聯繫人

  • 主要聯繫信息
  • 免責聲明
目錄
Product Code: D9A7

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

Table of Contents

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
  • Legal Disclaimer