Dreaming Of Others: Latent Teammate Modeling In World Models For Multi-Agent Reinforcement Learning
arXiv:2605.31361v1 Announce Type: cross Abstract: In cooperative multi-agent reinforcement learning (MARL), agents must coordinate with partners whose internal policies and intentions are not directly observable. While world models such as Dreamer have demonstrated strong generalization and sample efficiency in single-agent settings, their application to MARL remains limited by an inability to handle teammate-induced uncertainty. We propose a new perspective: treat teammates as structured, learnable components within the agent's world model. We introduce an architecture that factorizes the lat
The increasing complexity of multi-agent tasks in AI is driving the need for more sophisticated coordination mechanisms, pushing research beyond single-agent paradigms.
This research advances multi-agent AI by enabling agents to better predict and adapt to teammates, crucial for complex cooperative systems in diverse applications.
AI systems can now incorporate explicit models of other agents' intent and policy, moving beyond reactive coordination to proactive, predictive collaboration.
- · AI agents developers
- · Robotics industry
- · Logistics and automation
- · Simple reactive multi-agent systems
Improved performance and robustness in cooperative multi-agent reinforcement learning tasks.
Accelerated development of complex autonomous systems capable of sophisticated team play in real-world environments.
Enhanced AI capabilities for strategic decision-making and collaborative problem-solving across various sectors, potentially impacting human-agent teaming dynamics.
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