We conducted a Monte Carlo simulation study on the application of deep reinforcement learning methods for portfolio management with predictable returns and costly transactions. Our findings reveal varying algorithm performance, emphasizing the need to restrict asset samples for sufficient DRL performance. We also contributed to the Julia programming language ecosystem by providing codes for work replication and extension.