This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open scholarly metadata sources and uses AI-assisted classification to identify signals related to measurement, policy narratives, outcomes, implementation, industrial adoption, and verification.
The goal is not only to discover papers, but to observe how GX research is distributed across research substance, implementation narratives, external expectations, implementation substance, and judgment formation.
Summaries are AI-assisted. Always refer to the original paper for authoritative conclusions.
Showing 1–1 of 1 papers
Junhao Ren, Honglin Gao, Lan Zhao +3
This study develops a multi-agent reinforcement learning framework for self-interested microgrids in P2P electricity trading, addressing renewable generation and demand uncertainties. Each microgrid independently bids price and quantity, op…
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