Natural Policy Gradients In Reinforcement Learning Explained

W.J.A. van Heeswijk

Natural Policy Gradients In Reinforcement Learning Explained

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arxiv 2209.01820 v1 pith:UMT6S4AR submitted 2022-09-05 cs.LG math.OC

W.J.A. van Heeswijk This is my paper · ORCID

classification cs.LGmath.OC

keywords policygradientsnaturallearningoptimizationreinforcementaimsbehind

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Traditional policy gradient methods are fundamentally flawed. Natural gradients converge quicker and better, forming the foundation of contemporary Reinforcement Learning such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). This lecture note aims to clarify the intuition behind natural policy gradients, focusing on the thought process and the key mathematical constructs.

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Efficient Hypergradient Descent for Inverse Reinforcement Learning
cs.LG 2026-08 conditional novelty 5.0 of 10

For maximum-likelihood IRL, the inner-problem Hessian at a realizable optimum equals the temperature-scaled trajectory Fisher matrix, which enables a scalable sketched hypergradient method.

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