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cs.AI2026
Imagine to Ensure Safety in Hierarchical Reinforcement Learning
Gregory Gorbov, Artem Latyshev, Aleksandr I. Panov
This work investigates the safe exploration problem in reinforcement learning, where an agent must maximize cumulative performance while simultaneously satisfying safety constraint…
cs.AI2025
Safe Planning and Policy Optimization via World Model Learning
Artem Latyshev, Gregory Gorbov, Aleksandr I. Panov
Reinforcement Learning (RL) applications in real-world scenarios must prioritize safety and reliability, which impose strict constraints on agent behavior. Model-based RL leverages…
cs.AI2025
CrafText Benchmark: Advancing Instruction Following in Complex Multimodal Open-Ended World
Zoya Volovikova, Gregory Gorbov, Petr Kuderov +2
Following instructions in real-world conditions requires the ability to adapt to the world's volatility and entanglement: the environment is dynamic and unpredictable, instructions…