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Research questionHow can decentralized heterogeneous robots combine round-level policy reasoning with tick-level local control without destabilizing navigation learning?Decentralized robots must translate infrequent policy updates into reliable low-level actions while their local controllers continue adapting. Different policy agents and shared feedback add coordination challenges without a central action planner.
AI
Machine Learning
Multi-agent Systems
Reinforcement Learning
Robotics
Latest papersRecent research connected to this question, newest first.A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local LearningThe setting is a fixed NetLogo–Python simulation of three decentralized heterogeneous robots. Each robot uses an independent LLM policy agent, UCB-based refinement selection, and Double DQN for tick-level actions; LLMs generate or refine policies at round level, and robots communicate through shared round summaries. The evidence is descriptive, configuration-level results from four configurations over 30 rounds and 90 correlated robot–round records, with no broader deployment evidence supplied.research paper · Sep 4, 2026
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