Reinforcement Learning
Reinforcement learning (RL) is a type of machine learning that is inspired by the way animals and humans learn from their environment through trial and error. In RL, an agent interacts with an environment, making observations and taking actions in response to those observations. The environment provides feedback in the form of rewards or penalties, which the agent uses to update its behavior. By repeating this process over time, the agent learns to make decisions that lead to higher rewards and achieve its objectives, even in complex and dynamic environments.
REINFORCEMENT LEARNING AND DEEP REINFORCEMENT LEARNING : THE POWER OF ADVANCED AI
COMPARISON BETWEEN DRL (Deep Reinforcement Learning) AND RL (Reinforcement Learning)
Deep Reinforcement Learning goes beyond traditional Reinforcement Learning (RL). Whereas traditional RL generally requires pre-labeled data for learning, DRL is able to learn autonomously by actively exploring its environment. This autonomy enables it to shine in complex tasks where intelligent decisions are essential. DRL uses deep neural networks to model more complex decision policies.
WHAT IS DEEP REINFORCEMENT LEARNING?
Deep Reinforcement Learning (DRL) is a major advance in artificial intelligence (AI) that reinvents the way machines learn and make decisions. It combines machine learning and reinforcement techniques to enable learning systems to make autonomous decisions based on their interaction with the environment in which they evolve. This approach is distinguished by its use of deep neural networks, enabling agents to model sophisticated decision policies. At Delfox, we use this technology to solve complex problems in the aerospace and defense industries.
HOW DOES DRL WORK?
Deep Reinforcement Learning works on a simple but powerful principle. Our learning systems (agents) interact with a virtual environment (simulation) in which they make decisions to bring about long-term, cumulative reward. This iterative interaction is the key to autonomous learning. Using reinforcement algorithms and policy-based methods, our agents calculate the value of actions based on expected rewards. Deep neural networks enable these agents to represent complex decision policies and learn by trial and error. The advantages of DRL lie in its ability to make intelligent decisions in constantly changing environments.
COMPARISON BETWEEN DRL AND OTHER TECHNOLOGIES
Unlike other AI approaches, such as supervised and unsupervised learning, DRL focuses on learning for decision-making and planning. It does not require large quantities of labeled data, as is the case with supervised learning. Moreover, it differs from unsupervised learning, which is primarily concerned with discovering patterns in data. DRL excels in situations where strategic decisions need to be made autonomously.
OUR MASTERY OF TECHNOLOGY
Since our creation in 2018, we have been exploiting Deep Reinforcement Learning to solve complex problems in the Aeronautics, Space and Defense industries. We have worked with ArianeGroup on automatic satellite detection, with DGA, Thales and Dassault for the MMT (Man-Machine Teaming) project, which aims to develop technological building blocks for the future Franco-German "SCAF" fighter aircraft. Our expertise and commitment to innovation enable us to design tailor-made solutions that precisely meet your needs. We've been working tirelessly for several years to offer an innovative solution to the problems associated with autonomy in industry: REALMIND. Realmind is a platform for training autonomous decision-making systems capable of solving complex problems. If you would like to harness the potential of DRL for your company, please contact us. We look forward to working with you to create innovative, high-performance solutions.