Design a vision-based reinforcement learning model that enables a robotic system to detect, track, and physically intercept a fly in 3D space. The model combines a lightweight object-detection backbone for real-time fly localization with a learned control policy (e.g., PPO or SAC) that outputs motor commands to maneuver a catching mechanism (e.g.,

Design a reinforcement learning architecture for autonomous vulnerability discovery in security capture-the-flag (CTF) challenges, building on the Agent Web Model's hierarchical abstraction layers. The system will model CTF environments as Markov decision processes and develop RL algorithms (e.g., deep Q-learning or PPO with hierarchical extensions

Design a deep reinforcement learning algorithm capable of incrementally expanding its action space as new actions become available (e.g., new abilities unlocked in video game levels), without retraining from scratch or catastrophically forgetting previously learned action-value mappings.