ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL
Authors: name: Egor Cherepanov, name: Alexey K. Kovalev, name: Aleksandr I. Panov

Real-world robotic agents must act under partial observability and long
horizons, where key cues may appear long before they affect decision making.
However, most modern approaches rely solely on instantaneous information,
without incorporating insights from the past. Standard recurrent or transformer
models struggle with retaining and leveraging long-term dependencies: context
windows truncate history, while naive memory extensions fail under scale and
sparsity. We propose ELMUR (External Layer Memory with Update/Rewrite), a
transformer architecture with structured external memory. Each layer maintains
memory embeddings, interacts with them via bidirectional cross-attention, and
updates them through an Least Recently Used (LRU) memory module using
replacement or convex blending. ELMUR extends effective horizons up to 100,000
times beyond the attention window and achieves a 100% success rate on a
synthetic T-Maze task with corridors up to one million steps. In POPGym, it
outperforms baselines on more than half of the tasks. On MIKASA-Robo
sparse-reward manipulation tasks with visual observations, it nearly doubles
the performance of strong baselines. These results demonstrate that structured,
layer-local external memory offers a simple and scalable approach to decision
making under partial observability.