5 papers
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…
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…
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…
Safe Policy Exploration Improvement via Subgoals
Brian Angulo, Gregory Gorbov, Aleksandr Panov +1
Reinforcement learning is a widely used approach to autonomous navigation, showing potential in various tasks and robotic setups. Still, it often struggles to reach distant goals w…
Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning
Brian Angulo, Gregory Gorbov, Aleksandr Panov +1
While reinforcement learning algorithms have had great success in the field of autonomous navigation, they cannot be straightforwardly applied to the real autonomous systems withou…