  In this study, we have developed a dynamic asset allocation investment
strategy using reinforcement learning techniques. To begin with, we have
addressed the crucial issue of incorporating non-stationarity of financial time
series data into reinforcement learning algorithms, which is a significant
implementation in the application of reinforcement learning in investment
strategies. Our findings highlight the significance of introducing certain
variables such as regime change in the environment setting to enhance the
prediction accuracy. Furthermore, the application of reinforcement learning in
investment strategies provides a remarkable advantage of setting the
optimization problem flexibly. This enables the integration of practical
constraints faced by investors into the algorithm, resulting in efficient
optimization. Our study has categorized the investment strategy formulation
conditions into three main categories, including performance measurement
indicators, portfolio management rules, and other constraints. We have
evaluated the impact of incorporating these conditions into the environment and
rewards in a reinforcement learning framework and examined how they influence
investment behavior.
