Learn about the architecture and training process of a neural network-based dogfight agent developed using a custom RTDynamics simulation environment, and how it compares to a rule-based controller. Request your whitepaper here.
Dogfight between a traditional rule-based AI pilot and a neural network-controlled fighter trained with reinforcement learning (PPO algorithm).
The latest update to EWAWS adds four new example configurations for modeling artillery shells and rockets. These models include key aerodynamic and environmental effects relevant to projectile simulation and fire control applications.
We’re thrilled to announce a major milestone—RTD’s simulation model libraries are now successfully integrated with Python-based machine learning frameworks. This opens the door to powerful new possibilities in AI training for aerospace applications.
RTDynamics plug-in for VR-Forces 5.1.1d is now available to users under maintenance.
RTDynamics plug-in for VR-Forces 5.1.1c is now available to users under maintenance.