DECISION-MAKING AUTONOMY FOR
EXPLORE OUR LEADING AUTONOMY-AS-A-SERVICE PLATFORM TO BUILD
EFFICIENT AND EXPLAINABLE AUTONOMOUS SYSTEMS
The world is shifting from automation to autonomy
Existing systems are overly static and not suited to the dynamic needs of smart industry.
So we have built the first platform to train AI models to train autonomous decision-making systems.
Realmind is a software platform to train autonomous decision-making systems.
It yields on Deep Reinforcement Learning (DRL) that allows to learn on synthetic data and then learn by trial and error to converge to an optimal strategy.
Unlike supervised learning approaches such as deep learning, DRL does not require the prior constitution of databases. The learning model, named agent and modeled by a neural network, must therefore learn to interact with the simulation environment. This is done by means of observations (the inputs of the neural network) and by actions (the outputs of the neural network). During learning, the agent is led to explore by itself the environment in which it evolved.
Our platform offers several simulation environments to connect with, such as Unity.
Physical systems are then emulated inside the simulator to be trained through the platform.
Bringing autonomy to the industry
Delfox’s autonomous systems are trained to: assist operators through complex missions act autonomously to complete assigned tasks
Autonomous cooperation of drones and fighters for target scouting under communication constraints in a hostile environment.
Improve the efficiency of production lines and reduce human supervision and intervention during the various manufacturing stages.
Space Collision Avoidance
Assess the potential risk of collision and propose an accurate and autonomous avoidance and orbit change maneuver for intercept trajectories.
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.
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