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.
Natural Policy Gradients In Reinforcement Learning Explained
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abstract
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
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.