5 papers · 1 filter
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…
Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environments
Zoya Volovikova, Alexey Skrynnik, Petr Kuderov +1
In this study, we address the issue of enabling an artificial intelligence agent to execute complex language instructions within virtual environments. In our framework, we assume t…