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• Moscow, Russia o.bulichev@innopolis.ru Alexey Kovalevclaim 72%
• Recent advances in multi-agent reinforcement learning (MARL) have demonstrated success in numerous challenging domains and environments, but typically require specialized models for each task. In this workclaim 72%
• Multi-agent Learning; Reinforcement Learning; Multi-Task Learn- ing 1 INTRODUCTION Multi-agent reinforcement learning (MARL) has made significant progress in solving complex tasks such as competitive games like StarCraft or cooperative robotic tasks such as multi-agent pathfind- ing. Howeverclaim 72%
• seen in the training time that are different in the number of agents, 1Code: https://github.com/Cognitive-AI-Systems/marl-gptclaim 72%
• Figure 1: Spider plot demonstrating the relative performance of all evaluated approaches on three different environments – SMACv2, Google Research Football (GRF) and POGEMA. map layout etc. In this workclaim 72%
• 100M 1 Expert policy training 2 Trajectory collection 1B 400M sampling Figure 2: The general pipeline begins with training expert policies across diverse MARL environments using domain-appropriate algorithms (e.g., IPPO, RHCR)claim 72%

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