AINeutralarXiv – CS AI · 7h ago6/10
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Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
Researchers provide theoretical foundations for why linear recurrent neural networks excel as memory units in partially observable reinforcement learning environments. The study demonstrates that linear filters can exactly reproduce belief vectors in hidden Markov models under deterministic conditions and nearly eliminate state ambiguity, offering mathematical justification for their empirical success.