5 papers
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…
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…