is the implementation of this M-Step projector. It is a neural network specifically trained to minimize the KL-divergence between its current output distribution and the "ideal" distribution calculated in the E-step.
In the rapidly evolving field of Deep Reinforcement Learning (DRL), the tension between sample efficiency and algorithmic stability has long been a bottleneck. While traditional actor-critic methods have dominated the landscape, a specific niche of algorithms known as —and by extension, architectures referred to as MPO-NETs —has emerged as a robust alternative. mponetbr
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