7 papers
CoRL-MPPI: Enhancing MPPI With Learnable Behaviours For Efficient And Provably-Safe Multi-Robot Collision Avoidance
Stepan Dergachev, Artem Pshenitsyn, Aleksandr Panov +2
Decentralized collision avoidance is a core challenge for scalable multi-robot systems. A promising approach to this problem is Model Predictive Path Integral (MPPI) control - a fr…
Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
Valeriy Vyaltsev, Alsu Sagirova, Anton Andreychuk +5
Multi-agent pathfinding (MAPF) is a widely used abstraction for multi-robot trajectory planning problems, where multiple homogeneous agents move simultaneously within a shared envi…
MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning
Maria Nesterova, Mikhail Kolosov, Anton Andreychuk +6
Recent advances in multi-agent reinforcement learning (MARL) have demonstrated success in numerous challenging domains and environments, but typically require specialized models fo…
CAMAR: Continuous Actions Multi-Agent Routing
Artem Pshenitsyn, Aleksandr Panov, Alexey Skrynnik
Multi-agent reinforcement learning (MARL) is a powerful paradigm for solving cooperative and competitive decision-making problems. While many MARL benchmarks have been proposed, fe…
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…
MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov +1
Multi-agent pathfinding (MAPF) is a problem that generally requires finding collision-free paths for multiple agents in a shared environment. Solving MAPF optimally, even under res…