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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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1 result for “CollaQ”
Multi-Agent Collaboration via Reward Attribution Decompositioncs.LG

CollaQ improves multi-agent performance in StarCraft by 40% with fewer samples.

problem Limited generalization and high training rounds in multi-agent reinforcement learning.
method Formulates multi-agent collaboration as joint optimization on reward assignment, decomposes Q-function into self and interactive terms, and uses MARA loss.
result Improves win rate by 40% in StarCraft maps with same number of samples compared to state-of-the-art techniques.
multi-agent reinforcement learningreward attributionCollaQStarCraftad hoc team play
2020-10-16abs ↗pdf ↗