arXiv:2509.10656v2 Announce Type: replace-cross Abstract: For groups of autonomous agents to achieve a particular goal, they must engage in coordination and long-horizon reasoning. Rather than relying on complex reward functions and explicit cooperation mechanisms, we ask what minimal ingredients are required for effective coordination and exploration to emerge in multi-agent settings. We investigate this question through self-supervised goal-reaching, where agents aim to maximize the likelihood of visiting a goal state rather than maximizing a reward. Despite a sparse feedback signal, we pres
The research builds on rapid advancements in self-supervised learning and multi-agent systems, pushing the boundaries of autonomous cooperation without explicit reward engineering at a critical time for AI agent development.
This research outlines a pathway to more robust and generalized AI agents capable of complex coordination and exploration, significantly impacting the viability and deployment of autonomous systems in diverse fields.
The reliance on pre-defined complex reward functions for multi-agent systems may diminish, enabling more emergent and adaptive behaviors through self-supervised goal-reaching.
- · AI research labs
- · Robotics companies
- · Developers of multi-agent systems
- · Logistics and supply chain optimization
- · Companies reliant on highly-supervised, hand-engineered multi-agent solutions
- · Developers of simple rule-based AI systems
Multi-agent systems will become more adaptable and capable of solving complex problems in unstructured environments.
This could accelerate the deployment of autonomous systems in real-world scenarios like automated warehouses, drone swarms, and intelligent infrastructure.
These more capable autonomous agents could initiate new forms of economic activity and productivity gains, while also raising new ethical and control challenges.
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Read at arXiv cs.AI