Policy gradient methods belong to the category of artificial intelligence and are used in particular in the field of machine learning. They help computers to independently find solutions to complex problems without being given detailed rules by humans beforehand.
Imagine a robot learning to find the best way through a maze. Using policy gradient methods, the robot tries out different paths and receives a score for each attempt - for example, points for finding the exit quickly. Based on these points, the robot gradually improves its strategy until it has found the optimal path. The special feature: The method does not simply try out all the possibilities, but instead specifically adapts the robot's "decision rules" in order to achieve better results.
Policy gradient methods are an important component of modern AI solutions - for example in the control of autonomous vehicles, in robotics or in computer games. These methods enable machines and programmes to react flexibly to new situations and learn from their experiences. This makes policy gradient methods a key tool for innovative technologies of the digital future.
Deutsch 
English (UK)