Researchers used deep reinforcement learning to train an agent to make profitable trading decisions in high-frequency markets. They created a realistic trading environment using historical data and trained the agent to maximize trading returns using a specific learning architecture. By testing the agent with different levels of noise in trading signals, they found that it outperformed a basic trading strategy, showing that the agent learned an effective trading strategy for inventory management and order placement.