3 papers
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…
cs.LG2025
A New Perspective on Transformers in Online Reinforcement Learning for Continuous Control
Nikita Kachaev, Daniil Zelezetsky, Egor Cherepanov +2
Despite their effectiveness and popularity in offline or model-based reinforcement learning (RL), transformers remain underexplored in online model-free RL due to their sensitivity…
cs.LG2025
Re:Frame -- Retrieving Experience From Associative Memory
Daniil Zelezetsky, Egor Cherepanov, Alexey K. Kovalev +1
Offline reinforcement learning (RL) often deals with suboptimal data when collecting large expert datasets is unavailable or impractical. This limitation makes it difficult for age…