A DEEP REINFORCEMENT LEARNING METHOD FOR GENERATION OF ENVIRONMENTAL FEATURES FOR VULNERABILITY ANALYSIS AND IMPROVED PERFORMANCE OF COMPUTER VISION SYSTEMS

摘要

Described is a system for generating environmental features using deep reinforcement learning. The system receives a policy network architecture, initialization parameters, and a simulation environment that models a trajectory of a target system through a physical environment. Landmark features sampled from the policy network are initialized, and a trained policy network is generated by training the policy network using a reinforcement learning algorithm. A set of environmental features are generated using the trained policy network and displayed on a display device.