arXiv:2605.31261v1 Announce Type: new Abstract: The family of linear recurrent neural networks has shown strong performance as recurrent memory units in partially observable reinforcement learning. We provide a theoretical justification for their empirical effectiveness by constructing and studying two linear filters: (i) the first exactly reproduces the pre-softmax logits of the belief vector in a hidden Markov model (HMM) under a deterministic transition matrix, thereby serving as a sufficient statistic for optimal policy learning, (ii) the second achieves vanishing state-decoding error unde
This research provides theoretical grounding for observed empirical success in a specific area of AI, indicating a maturation of understanding in recurrent neural networks.
Understanding the theoretical underpinnings of linear recurrent memory in partially observable reinforcement learning can lead to more robust, efficient, and explainable AI systems.
This theoretical justification allows for more principled design and optimization of recurrent memory units for sequential decision-making in complex environments, moving beyond purely empirical approaches.
- · AI researchers
- · Reinforcement learning developers
- · Robotics companies
- · Autonomous systems developers
Improved performance and reliability of AI agents operating in real-world, partially observable environments.
Accelerated development of more complex and adaptive AI systems for tasks requiring long-term memory and decision-making.
Enhanced AI capabilities contributing to advancements in areas like autonomous vehicles, sophisticated industrial automation, and advanced AI agents.
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Read at arXiv cs.LG