Deep Reinforcement Learning

AI-based C-UAV Swarm Defense

Artificial Twin developed a high-fidelity, simulation-based RL control system to support decision-making in drone defense scenarios involving large-scale kamikaze UAV swarms. Designed as a threat-prioritization and coordination layer, the RL policy learned to minimize zone damage more effectively than traditional heuristics, demonstrating adaptability under uncertainty and noisy input conditions. Problem Context With the rapid rise of low-cost autonomous UAVs, defense systems must now contend with the possibility of large-scale kamikaze drone attacks that overwhelm conventional rule-based responses.

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DIAMBRA AI Tournament Platform

A Competitive AI Platform for Deep Reinforcement Learning Artificial Twin partnered with DIAMBRA to architect and develop an advanced platform for training, deploying, and evaluating intelligent agents through Deep Reinforcement Learning (DeepRL) in arcade-style competitive simulations. The collaboration focused on delivering a robust, modular system capable of supporting large-scale experimentation and community participation. DIAMBRA offered a novel combination of research-grade training environments and consumer-facing esports-style engagement, creating a hybrid product with applications across AI research, education, and competitive development.

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