Second-Order Actor-Critic Methods for Discounted MDPs via Policy Hessian Decomposition

Researchers have introduced a new actor-critic reinforcement learning method that utilizes policy Hessian decomposition to better handle value approximation in discounted reward environments. By incorporating second-order information, this approach aims to improve convergence stability compared to traditional first-order policy gradient techniques.