Here’s Why 2020 Could Be Another Big Year for Reinforcement Learning Market

Here\'s Why 2020 Could Be Another Big Year for Reinforcement Learning Market

Reinforcement Learning Market
A new market study on Global Reinforcement Learning Market with + data Tables, Pie Chart & Graphs is published to provide complete assessment of the Market highlighting evolving trends, current-to-future scenario analysis and growth factors validated with experts view. The study breaks market by revenue and volume (wherever applicable) and price history estimates for Reinforcement Learning.

A new research document is added in HTF MI database of 76 pages, titled as ‘Reinforcement Learning – Startup Ecosystem Analysis’  with detailed analysis, Competitive landscape, forecast and strategies. The study covers geographic analysis that includes regions and important players/vendors such as Osaro, OpenAI, Acutronic Robotics, Wayve, Kindred,, Cerebri AI, Micropsi Industries, CogitAI, InstaDeep, Latent Logic, NeuDax, Ascent Robotics, Context Scout, Wasteless. The report will help you gain market insights, future trends and growth prospects for forecast period of 2020-2025

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Reinforcement learning is the next revolution in artificial intelligence (AI). As a feedback-driven and agent-based learning technology stack that is suitable for dynamic environments, reinforcement learning methodologies leverage self-learning capabilities and multi-agent potential to address issues that are unaddressed by other AI techniques. In contrast, other machine learning, AI techniques like supervised learning and unsupervised learning are limited to handling one task at a given time.

With the advent of Artificial General Intelligence (AGI), reinforcement learning becomes important in addressing other challenges like multi-tasking of intelligent applications across different ecosystems. The technology appears set to drive the adoption of AGI technologies, with companies futureproofing their AGI roadmaps by leveraging reinforcement learning techniques.

This report provides an analysis of the startups focused on reinforcement learning techniques across industries.
Startup Ecosystem
We describe the importance of agent-based learning methods and the core concepts related to reinforcement learning. The section includes a comparison of machine learning techniques, and an overview of topics like imitation learning, AGI, and model-based and model-free reinforcement learning algorithms. Further, we outline how reinforcement learning techniques can help address the current challenges of various industries in developing next-generation solutions.

The report includes an analysis of startups leveraging reinforcement learning algorithms in key sectors like automotive, retail, industrial, financial services, robotics, healthcare, IoT, food industry, and several others. We cover 39 startups, out of which some prominent startups like Osaro, Kindred, Micropsi Industries, Wayve, Cerebri AI, and OpenAI have been reported in detail.

Analysis of these startups has been undertaken basis their technologies, offerings, patenting activities, and future outlooks. Additionally, an overview of other startups including Latent Logic, NeuDax, Nnaisense PerimeterX, Deeplite, and Context Scout, which are leveraging reinforcement learning algorithms, has been covered in the ambit of the report.

Startups covered
: Osaro, OpenAI, Acutronic Robotics, Wayve, Kindred,, Cerebri AI, Micropsi Industries, CogitAI, InstaDeep, Latent Logic, NeuDax, Ascent Robotics, Context Scout, Wasteless

Key insights:
– Reinforcement learning is a prime technology for the of future self-learning, self-optimizing and other self-driving abilities needed in autonomous applications across industries.
– Automotive, retail, ecommerce, and robotics is crowded with startups developing reinforcement learning techniques.
– Reinforcement learning can address the requirements related to dynamic decision-making in autonomous vehicles targeting level 5 autonomy.
– Other sectors exploring reinforcement learning are healthcare, financial services, food industry, manufacturing, education and telecom.
– Startups that are offering reinforcement learning techniques in robotic solutions are focusing on robots as a service business model.

Key questions addressed:
– What are the advantages offered by reinforcement learning algorithms in comparison with the other machine learning models?
– What are the basic concepts in reinforcement learning?
– How can reinforcement learning address the technical requirements of different sectors and emerging applications?
– What is nature and size of the startup ecosystem for reinforcement learning and how is it changing the landscape of traditional artificial intelligence?
– What are the target industries and application areas for the new companies?
– What are the technologies, future roadmap, partnership activities, limitations, and offerings of the reinforcement learning- focused startups across industries?
– What are the potential collaboration opportunities for different industry segments to leverage reinforcement learning solutions for their businesses?

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1. Executive Summary
2. Introduction
3. Taxonomy of Reinforcement Learning Algorithms
3.1. Model-based and Model-free Methods
3.2. Imitation Learning
3.3. A


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