Summary of Global Convergence Of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction, by Jie Feng et al.
Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction
by Jie Feng, Ke Wei, Jinchi Chen
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a new variant of the natural policy gradient (NPG) algorithm, called NPG-HM, which utilizes the Hessian-aided momentum technique to reduce variance and stochastic gradient descent for sub-problem solution. The proposed method achieves global last iterate epsilon-optimality with an O(ε^(-2)) sample complexity, outperforming other state-of-the-art policy gradient methods in Mujoco-based environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new version of the natural policy gradient algorithm called NPG-HM. It helps computers learn and make good choices by using two special techniques: Hessian-aided momentum and stochastic gradient descent. The new method is very good at finding the best solution and can do it quickly, even when there’s not much data to work with. This is important for things like robots learning how to move. |
Keywords
* Artificial intelligence * Stochastic gradient descent
