Natural Policy Gradient

Natural Policy Gradient (NPG) methods aim to efficiently optimize policies in reinforcement learning by leveraging the geometry of the policy space, leading to faster convergence compared to standard policy gradient methods. Current research focuses on extending NPG's applicability to complex scenarios, including infinite state spaces, partially observable environments, and multi-objective or constrained tasks, often employing recurrent neural networks or linear function approximation for policy representation and value function estimation. These advancements improve the scalability and robustness of reinforcement learning algorithms, impacting various fields such as robotics, resource management, and game theory through more efficient and effective learning of optimal control strategies.