Policy Network

Policy networks are neural networks used in reinforcement learning to map states to actions, aiming to learn optimal control policies for agents in various environments. Current research focuses on improving efficiency and generalization of these networks, exploring architectures like diffusion models and leveraging techniques such as meta-learning, constrained optimization, and Lipschitz-bounded parameterizations to enhance robustness and sample efficiency. This work is significant for advancing artificial intelligence, particularly in robotics and autonomous systems, by enabling the development of more adaptable, reliable, and efficient agents capable of handling complex tasks and uncertain environments.