Michael Muehlebach
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2026
Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
This paper theoretically justifies the strong performance of linear recurrent neural networks as memory units in partially observable reinforcement learning by constructing specific linear filters that serve as sufficient statistics for optimal policy learning.
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cs.LGcs.AIstat.MLRecentMay 29, 2026
Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
Yike Zhao, Onno Eberhard, Malek Khammassi, Ali H. Sayed +1 more
This paper theoretically justifies the strong performance of linear recurrent neural networks as memory units in partially observable reinforcement learning by constructing specific linear filters tha…
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