Leveraging Deep Reinforcement Learning For Active Shooting Under Open-World Setting

Recent advances in Deep Reinforcement Learning (DRL) led to the development of powerful agents that can learn how to perform complicated tasks in an end-to-end fashion operating directly on raw unstructured data, e.g., images. However, the real world performance of such methods critically relies on the quality of the simulation environments used for training them. The main contribution of this paper is the development of a realistic simulation environment, by employing a state-of-the-art graphics engine, for training DRL agents that are able to control a drone for performing active shooting. In contrast with previous approaches, that solely relied on simplistic constrained datasets, the environment employed in this work supports a challenging open-world setting, providing a solid step towards developing effective RL methods for various drone control tasks. An appropriate reward shaping approach is also introduced in this work, ensuring that the agent will behave as expected, avoiding erratic movements, as demonstrated through the conducted experiments.