Reinforcement Learning for Trade Execution and Market Making

A classical execution schedule is an open-loop policy: it commits to a plan before seeing anything, and reinforcement learning's entire value here is closing that loop. This path measures what closing it is actually worth, shows how a mis-sized penalty makes leaving part of the order unexecuted rationally optimal, and demonstrates that one unverifiable queue assumption moves the simulated fill rate by a factor of two. Every figure is computed.