November 8, 2025Open Access

Policy Optimization Algorithms in a Unified Framework

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Analysis reveals optimal policy design in algorithm implementation, indicating reduced misuse through better understanding.

Policy optimization algorithms enhance understanding of stochastic processes, particularly discounted and average rewards.
Key insights include a case study demonstrating how algorithm design influences outcomes in policy optimization.
Application of generalized ergodicity theory aids comprehension of steady-state behaviors in Markov decision processes.
Employing perturbation analysis identifies common errors in implementing policy optimization algorithms effectively.

Shuang Wu (2025) studied this question.

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