2 papers
cs.LG2026
ALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning
Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov +1
In partially observable reinforcement learning (RL), a later observation can make stored information obsolete or change what it implies for the next decision. Memory architectures…
cs.LG2026
Memory Retention Is Not Enough to Master Memory Tasks in Reinforcement Learning
Oleg Shchendrigin, Egor Cherepanov, Alexey K. Kovalev +1
Effective decision-making in the real world depends on memory that is both stable and adaptive: environments change over time, and agents must retain relevant information over long…