Deep Reinforcement Learning

The world is shifting from automation to autonomy

Explore the revolution in Aerospace & Defense (ASD) with our pioneering solutions based on Deep Reinforcement Learning (DRL). This innovative technology redefines spectral management with an intelligent and dynamic approach, constantly adjusted to real-time changes.

DRL’s ability to learn from synthetic data eliminates the need for a wealth of prior data, enabling rapid adaptation to changing spectrum conditions.

We support you throughout your AI projects with our experts in Machine Learning and Reinforcement Learning.


Bringing autonomy to the industry

Delfox’s autonomous systems are trained to: assist operators through complex missions act autonomously to complete assigned tasks

Application 1

Collaborative Combat

Autonomous cooperation of drones and fighters for target scouting under communication constraints in a hostile environment.

Application 2

Process optimization

Improve the efficiency of production lines and reduce human supervision and intervention during the various manufacturing stages.

Application 3

Space Collision Avoidance

Assess the potential risk of collision and propose an accurate and autonomous avoidance and orbit change maneuver for intercept trajectories.

Military Surveillance Officer Working on a City Tracking Operation in a Central Office Hub for Cyber Control and Monitoring for Managing National Security, Technology and Army Communications.

Application 4

Path planning

Find the most effective route from a location to another through several steps with multi-agents scenarios that brings additional complexity to this use-case.


Allowing machines to learn from their actions

Unlike supervised learning approaches such as deep learning, reinforcement learning does not require the prior constitution of databases. Instead, the learning is performed according to the trial-and-error principle within a simulation environment that generates the data set on the fly.

The learning model, named agent and modeled by a neural network, must therefore learn to interact with the simulation environment.

Contrary to supervised learning which requires extensive and static training data, DRL relies on simulation to train agents on data generated on-the-fly.

The agent is the centerpiece of DRL, representing the final product of the training process that is designed to be deployed in the real world.

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.


Our difference

Structured around solid skills on state-of-the-art science, marketing, design and business. We are constantly looking for new applications and challenges to make your company more competitive.

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