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arXiv AI By Zekang Wang, Zhe He, Gaojie Jin

Does Learning Recurrent Networks Matter in Partially Observable RL?

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The paper investigates whether a frozen recurrent network can provide sufficient memory for partially observable Markov decision processes (POMDPs). Using a fixed recurrent network combined with a trainable output module, the authors demonstrate that such networks can recall information over hundreds of steps and achieve competitive control performance across diverse POMDP tasks, while using fewer than half the trainable parameters of fully trainable recurrent networks. These findings suggest that learning the recurrent memory module is not always necessary, offering a simple, parameter‑efficient baseline for partially observable reinforcement learning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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