Second-Order Actor-Critic Methods for Discounted MDPs via Policy Hessian Decomposition
We address the discounted reward setting in reinforcement learning (RL). To mitigate the value approximation challenges in policy gradient methods, actor-critic approaches have been developed and are known to converge to stationary points under suitable assumptions. However, these methods rely on first-order updates. In contrast, second-order optimization provides principled curvature-aware updates that are proven to accelerate convergence, but its application in RL is limited by the computational complexity of Hessian estimation. In this work, we analyze second-order approximations for the actor update that leverage the full curvature information of the objective as much as possible. A s...
| Authors | Sanjeev Manivannan, Shuban V |
|---|---|
| Primary category | cs.LG |
| Published | 14 May 2026 |
| Last updated at source | 9 July 2026 |
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