[2502.10550] Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning

Computer Science > Machine Learning arXiv:2502.10550 (cs) [Submitted on 14 Feb 2025 (v1), last revised 4 Mar 2026 (this version, v3)] Title:Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning Authors:Egor Cherepanov, Nikita Kachaev, Alexey K. Kovalev, Aleksandr I. Panov View a PDF of the paper titled Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning, by Egor Cherepanov and 3 other authors View PDF HTML (experimental) Abstract: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 particularly evident in tabletop robotic manipulation, where memory is essential for solving tasks with partial observability and ensuring robust performance, yet no standardized benchmarks exist. To address this, we introduce MIKASA (Memory-Intensive Skills Assessment Suite for Agents), a comprehensive benchmark for memory RL, with three key contributions: (1) we propose a comprehensive classification framework for memory-intensive RL tasks, (2) we collect MIKASA-Base -- a unified benchmark that enables systematic evaluation of memory-enhanced agents across diverse scenarios, and (3) we develop MIKASA-Robo (pip install mikasa-robo-suite) -- a novel benchmark of 32 carefully designed...