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Nikita Kachaev

2 accepted papers

2026

Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning

ICLR 2026poster

Memory is crucial for enabling agents to tackle complex tasks with temporal and spatial dependencies. While many reinforcement learning (RL) algorithms incorporate memory, the field lacks a universal benchmark to assess an agent's memory capabilities across diverse scenarios. This gap is particularl…

Cited by 0SourcecodeScholar
2026

Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation

ICLR 2026poster

The incorporation of memory into agents is essential for numerous tasks within the domain of Reinforcement Learning (RL). In particular, memory is paramount for tasks that require the use of past information, adaptation to novel environments, and improved sample efficiency. However, the term ``memor…

Cited by 0SourceScholar