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Realmind
The most powerfull tool to design-train-evaluate autonomous systems
Realmind’s autonomous agents train in simulations to observe,
adapt and converge on key strategies to be applied in operating missions.
1. Connect any simulator
The first step with Realmind is then to connect the simulator.
Benefit from our of-the-shelf package » Realmind Connector for Unity » to get started with your first training in minutes. Or rely on our expertise in connecting tens of simulators to get started with your project in Unreal Engine, Anylogic, Gazebo, and more.
2. Setting up your training scenario
Leverage your expertise and rely on the abstraction of the Realmind scenario editor to define your problem in the most precise way. The Realmind Scenario Editor allows you to change the composition of the autonomous team, randomize the position and number of obstacles, define the location of agents, and more. There are countless possibilities for setting up the learning scenario to suit your use-case.
3. Configure the learning model
Realmind offers the ability to customize the learning process. Pick from our wide library of algorithms, which stays up-to-date with state-of-the art algorithms coming from the most renowned research institutions. Go further with our Neural Network Editor, which allows you to go beyond simple densely connected neural networks and lets you add attention blocks and convolution blocks.
4. Evaluate the performance of your autonomous agents
Once training begins, evaluate the model’s performance on the task at hand. Visualize its progress on easy-to-use charts, as well as agent behavior in our 3D visualization player. This will ensure that your autonomous agents behave as you intended and are ready to overcome any new situation with confidence.
5. Export your trained model to a physical vector
With Realmind’s export functionality, you can download the model in a standard and interoperable format. Realmind facilitates the task of your integration engineers by providing sample code for the main programming languages and platforms. The export also includes diagrams of the trained model so that operators can easily understand the inner-workings of the model.
