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EnliteAI
Vienna, Austria
A
1-10 Employees
-
Key takeaway
enliteAI is a specialized technology provider for Artificial Intelligence, focusing on Reinforcement Learning and Computer Vision. They are the creators of Maze, one of the first open-source frameworks for applied Reinforcement Learning.
Highlighted product
Core business
enliteAI - Reinforcement Learning, Computer Vision and Power Grid Optimization
enliteAI is a technology provider for Artificial Intelligence. We are the maker of Maze, an open-source framework for applied Reinforcement Learning and Detekt, a modern geospatial data platform for object detection in mobile mapping data supporting the entire asset management life cycle.
Phantasma Labs
Berlin, Germany
A
1-10 Employees
-
Key takeaway
The company, Phantasma Labs, specializes in enterprise-level reinforcement learning, providing high-performance models that help businesses optimize various planning processes. Their expertise in simulation science and scalable systems ensures that clients can effectively leverage AI for improved decision-making.
Highlighted product
Core business
Phantasma Labs | Enterprise-level reinforcement learning
With our models businesses can benefit from AI without needing their own big data
Winder.AI
Harrogate, United Kingdom
A
1-10 Employees
2013
Key takeaway
Winder.AI specializes in providing state-of-the-art reinforcement learning consulting and development, aimed at helping businesses harness the power of AI to enhance their operations. With a track record of collaborating with major industry leaders, Winder.AI is well-equipped to support organizations in launching and advancing their reinforcement learning projects.
Highlighted product
Service
Reinforcement Learning Services Services
Winder.AI provides state-of-the-art reinforcement learning consulting, research and development. We've worked for some of the largest names in the world and continue to deliver transformational insights for businesses around the world.
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Data Science and Artificial Intelligence in Parma
Parma, Italy
B
1-10 Employees
2019
Key takeaway
The company highlights the recent advancements in artificial intelligence, particularly through AlphaGo and OpenAI Five, which showcase how reinforcement learning algorithms can surpass human performance in complex games. The upcoming seminar will introduce the fundamental principles and techniques of reinforcement learning and explore its real-world applications.
Highlighted product
Core business
DS&AI Parma
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.
Highlighted product
Product
Technology – Delfox
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.
Highlighted product
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.
Highlighted product
Core business
Home – Data Maroc - A Community of Moroccan Data Enthusiasts
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.
Highlighted product
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
DIAMBRA | Dueling AI Arena
San Francisco, United States
B
1-10 Employees
2020
Key takeaway
DIAMBRA offers a unique platform for training AI agents in high-quality Reinforcement Learning environments, allowing them to compete in live video game tournaments. This innovative approach not only enhances the learning experience but also fosters a global community of coders who can showcase their AI's capabilities.
Highlighted product
Core business
DIAMBRA | Dueling AI Arena
The platform where AI agents compete in video games tournaments. Our unique ecosystem is based on an exclusive collection of high quality Reinforcement Learning Environments, and on our proprietary AI Tournaments Platform open to every coder worldwide.
AI Superior
Darmstadt, Germany
A
11-50 Employees
2019
Key takeaway
AI Superior specializes in AI services and software solutions, highlighting their expertise in reinforcement learning as part of their commitment to delivering effective AI applications. Their focus on generative AI development and complex machine learning models positions them to help businesses leverage advanced technologies for tangible results.
Highlighted product
Product
Deep Learning | Reinforcement Learning | Ai Superior
Deep learning helps data science collect and analyze necessary information while imitating how humans gain a particular type of knowledge.
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 | 4976 |
| Amount of suitable service providers | 4579 |
| Average amount of employees | 1-10 |
| Oldest suiting company | 2013 |
| Youngest suiting company | 2020 |
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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 DIAMBRA | Dueling AI Arena
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.
