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Multi-Agent Reinforcement Learning Framework for Autonomous Arbitrage Systems A Technical Analysis of the AAS Micro-Reinforcement Architecture
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| Multi-Agent Reinforcement Learning Framework for | |
| Autonomous Arbitrage Systems | |
| A Technical Analysis of the AAS | |
| Micro-Reinforcement Architecture | |
| Aayush Mehta | |
| Autonomous Alpha Swarm (AAS) — Technical Documentation v2.0 | |
| January 2026 | |
| Abstract | |
| This document presents a comprehensive mathematical analysis of the reinforcement learning mechanisms employed in the Autonomous Alpha Swarm (AAS) trading system. We formalize the multi-layered learning architecture consisting of (1) micro-reinforcement after each trade, (2) blend optimization at periodic intervals, (3) VLONE (Variance-adjusted Liquidity Opportunity Net Edge) component weight learning, and (4) QuantEngine factor weight optimization. The framework implements a novel hierarchical temporal difference approach where immediate trade outcomes propagate through exponential moving averages to affect agent fitness scores, which in turn influence position sizing and trade selection. We provide rigorous mathematical proofs for convergence properties under realistic mark |