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Applied Data Science Partners
London, United Kingdom
A
1-10 Employees
2016
Key takeaway
Applied Data Science Partners (ADSP) offers advanced deep reinforcement learning agents that can address real-world challenges through innovative data science solutions. Their expertise in AI and data science ensures that businesses can leverage these powerful techniques for long-term value.
Reference
Service
AlphaZero Science & Reinforcement Learning | ADSP
Access intelligent deep reinforcement learning agents that can help solve your real world challenges with data science solutions.
AgileRL
London, United Kingdom
A
1-10 Employees
2023
Key takeaway
AgileRL is dedicated to making reinforcement learning more accessible for creating advanced artificial intelligence systems. Their RLOps platform enhances the entire reinforcement learning process, allowing for significantly faster training and improved performance.
Reference
Core business
AgileRL
Revolutionising machine learning with RLOps: MLOps for reinforcement learning. Develop faster and achieve greater performance with the best RL tools.
DELFOX - PREDICTIVE TECHNOLOGIES
Bordeaux, France
A
1-10 Employees
2018
Key takeaway
Delfox focuses on advanced learning algorithms in artificial intelligence, particularly in the context of deep reinforcement learning, which is crucial for the development of autonomous systems. Their expertise in this area enhances the capabilities of their systems to assist operators and complete tasks independently, making them a key player in sectors like aeronautics, space, and defense.
Reference
Product
Technology – Delfox
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RL-LAB
Paris, France
A
1-10 Employees
-
Key takeaway
RL-LAB is dedicated to making Reinforcement Learning accessible to AI enthusiasts, highlighting that Grid World using Dynamic Programming is a fundamental example of this approach.
Reference
Core business
RL Lab – Reinforcement Learning made simple
Data Maroc
Rabat, Morocco
D
1-10 Employees
2019
Key takeaway
The company discusses reinforcement learning as a formal mathematical framework where an agent interacts with its environment, receiving reward values for its actions. This concept is particularly relevant in applications like medical image diagnosis.
Reference
Core business
Home – Data Maroc - A Community of Moroccan Data Enthusiasts
FUTURETEXT LTD
London, United Kingdom
A
1-10 Employees
-
Key takeaway
FeynLabs, led by Ajit Jaokar, who teaches Artificial Intelligence at the University of Oxford, focuses on implementing AI solutions through collaboration with research institutes and corporate R&D teams. They actively engage in projects related to reinforcement learning, among other AI applications.
Reference
Core business
FeynLabs.AI – Creating Innovative Solutions
WHO WE ARE Feynlabs is created by Ajit Jaokar, who is the course director of the Artificial Intelligence: Cloud and Edge Implementations course at the University of Oxford. WHAT WE DO We work with leading educational research institutes and corporate R & D teams for implementing
Prophysics - Artificial Intelligence Machine Learning en Big Data voor de bouwsector
Oudenbosch, Netherlands
A
1-10 Employees
2021
Key takeaway
The company text discusses the relationship between Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI), highlighting that Deep Learning is a subset of Machine Learning.
Reference
Core business
Machine Learning Archieven - Prophysics
Humans in the Loop
Sofia, Bulgaria
B
101-250 Employees
2017
Key takeaway
The company, Humans in the Loop, specializes in reinforcement learning with human feedback (RLHF), offering trained professionals to enhance large-scale models for language and vision. Their approach includes generating examples, ranking outputs, and testing models, ensuring improved accuracy and performance.
Reference
Product
Reinforcement learning with human feedback | Humans in the Loop
Train and improve your LLMs and other large-scale models for language and vision with our trained humans-in-the-loop. Use RLHF for generating examples, ranking outputs, and testing your models for vulnerabilities
Worksbot
Toyokawa, Japan
A
251-500 Employees
2011
Key takeaway
Worksbot Applications is a development company that specializes in Artificial Intelligence (AI) and Machine Learning technologies.
Reference
Core business
Worksbot | Software Development Company | Machine Learning | Artificial Intelligence | CRM | ERP
Darwin Edge AI
Lausanne, Switzerland
A
11-50 Employees
2021
Key takeaway
Darwin Edge specializes in deploying AI/ML techniques, including reinforcement learning, to edge devices, facilitating real-time data analytics and predictive capabilities. Their expertise in embedded AI solutions is particularly beneficial for applications in healthcare, automotive, retail, and manufacturing.
Reference
Service
Edge AI Expertise | Embedded AI Solutions - Darwin Edge Darwin Edge
Efficient deployment of AI algorithms on edge devices enabling real-time data analytics and predictive, reinforcement, and federated learning.
Technologies which have been searched by others and may be interesting for you:
Reinforcement Learning (RL) is a subset of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions, which helps it to optimize its strategy over time. Through trial and error, the agent explores various actions to maximize cumulative rewards. This approach is particularly useful in complex scenarios where traditional programming methods fall short. Applications of reinforcement learning can be found in robotics, game playing, and various optimization tasks, illustrating its capability to adapt and improve performance in dynamic settings.
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, which helps it understand the consequences of its choices. It utilizes a trial-and-error approach, optimizing its strategy over time to maximize cumulative rewards. At the core of RL is the concept of a policy, which defines the agent’s behavior in various states of the environment. The agent explores different actions and learns from the outcomes, adjusting its policy to improve performance. Techniques such as Q-learning and deep reinforcement learning enhance the agent's ability to navigate complex environments, making RL applicable in various domains, from robotics to game playing.
1. Robotics
Reinforcement Learning is extensively used in robotics for training agents to perform complex tasks. Robots can learn to navigate environments, manipulate objects, and cooperate with humans through trial and error, enhancing their efficiency and adaptability.
2. Game Playing
Another prominent application is in game playing. Reinforcement Learning algorithms have achieved remarkable success in mastering games like Chess and Go, where they learn optimal strategies through self-play and experience, often surpassing human capabilities.
3. Autonomous Vehicles
In the realm of autonomous vehicles, Reinforcement Learning helps in decision-making processes. It enables vehicles to learn from their surroundings and improve their navigation, making real-time adjustments to ensure safety and efficiency.
4. Financial Trading
Reinforcement Learning is increasingly applied in financial trading. Algorithms can learn from market dynamics and historical data to make strategic trading decisions, optimizing investment returns based on changing market conditions.
5. Healthcare
In healthcare, this technology is used for personalized treatment plans. By analyzing patient responses and outcomes, Reinforcement Learning can recommend tailored interventions that improve patient health over time.
1. Improved Decision-Making
Reinforcement Learning enables systems to make better decisions by learning from past experiences. Through trial and error, algorithms adapt and optimize actions based on rewards, leading to more effective strategies over time.
2. Flexibility and Adaptability
This approach is highly flexible, allowing it to be applied across various domains such as robotics, finance, and gaming. Reinforcement Learning models can adapt to changing environments and requirements, making them suitable for dynamic situations.
Reinforcement Learning (RL) presents several challenges that can hinder its effective implementation. One significant obstacle is the requirement for a vast amount of training data, as RL agents learn through trial and error, which can lead to lengthy training periods. Additionally, the exploration-exploitation dilemma complicates learning, as agents must balance between exploring new actions and exploiting known rewarding actions to optimize performance. Another challenge lies in the high dimensionality of state and action spaces, making it difficult for agents to generalize their learning. This often results in the need for sophisticated function approximation methods. Furthermore, RL can suffer from instability and convergence issues, especially in environments with sparse rewards or high variability, which can make it challenging to design robust and reliable agents.
Some interesting numbers and facts about your company results for Reinforcement Learning
| Country with most fitting companies | United Kingdom |
| Amount of fitting manufacturers | 7984 |
| Amount of suitable service providers | 7938 |
| Average amount of employees | 1-10 |
| Oldest suiting company | 2011 |
| Youngest suiting company | 2023 |
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Some interesting questions that has been asked about the results you have just received for Reinforcement Learning
What are related technologies to Reinforcement Learning?
Based on our calculations related technologies to Reinforcement Learning are Big Data, E-Health, Retail Tech, Artificial Intelligence & Machine Learning, E-Commerce
Who are Start-Ups in the field of Reinforcement Learning?
Start-Ups who are working in Reinforcement Learning are AgileRL, Prophysics - Artificial Intelligence Machine Learning en Big Data voor de bouwsector, Darwin Edge AI
Which industries are mostly working on Reinforcement Learning?
The most represented industries which are working in Reinforcement Learning are IT, Software and Services, Education, Other, Consulting, Human Resources
How does ensun find these Reinforcement Learning Companies?
ensun uses an advanced search and ranking system capable of sifting through millions of companies and hundreds of millions of products and services to identify suitable matches. This is achieved by leveraging cutting-edge technologies, including Artificial Intelligence.