RTDynamics has recently announced the availability of a reinforcement learning environment which runs on the AgileRL's Arena platform. Arena is o modern RLOPS platform with lightning-fast learning, automatic tuning via evolutionary hyperparameter optimization and one click deployment. Here is an HOWTO video, which demonstrates the steps from environment upload and initial configuration up to optimized agent training.
RTDynamics has teamed up with AgileRL, a London-based Reinforcement Learning specialist, to develop a custom gym training environment. Together, they have successfully demonstrated the training of an AI drone agent using AgileRL's Arena platform integrated with RTDynamics simulation models. The trained agent controls a quadcopter to intercept an incoming drone and defend a moving ground vehicle.
RTDynamics has taken its prototype integration of large language model (LLM)-based AI agents in the VR-Forces simulation environment to the next level. In this latest demonstration, our AI agents go beyond understanding and executing natural language commands for individual entities — they can now coordinate and control entire formations of aircraft.
RTDynamics has developed a prototype AI agent based on large language models (LLMs) and integrated it into the VR-Forces simulation environment, a widely used computer-generated forces (CGF) platform for military training, research, and operational analysis.
This prototype demonstrates how LLM-based agents can enhance simulation interactivity. By incorporating LLM-powered AI agents into VR-Forces, it is possible to enable more intuitive, natural language control of simulated entities.
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.