Policy Parameterization

Policy parameterization in reinforcement learning focuses on efficiently representing and learning the mapping from states to actions within a policy. Current research emphasizes improving sample efficiency and convergence rates through novel architectures like low-rank matrix models and specialized neural networks (e.g., those incorporating Lipschitz constraints or graph neural networks), as well as advanced algorithms such as mirror descent and primal-dual methods. These advancements aim to address challenges like the curse of dimensionality and instability in policy optimization, ultimately leading to more robust and efficient reinforcement learning agents for various applications, including robotics and resource management.