Trust Region Policy Optimization: Mathematical Foundations, Monotonic Improvement Guarantees, and Conjugate Gradient Updates
Trust Region Policy Optimization: Mathematical Foundations, Monotonic Improvement Guarantees, and Conjugate Gradient Updates In policy gradient reinforcement learning, optimization dynamics differ fundamentally from standard supervised learning. In supervised regression or classification, the underlying data distribution $P(x, y)$ remains stationary throughout training; a sub-optimal parameter update merely yields high loss on the current batch without corrupting future sample collection. In re
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