Before revision:  This study formulates a portfolio trading process as a Markov decision process in which the agent can learn about the financial market environment, and it identifies a deep neural network structure as an approximation of the Q-function. To ensure applicability to real-world trading , we devise three novel techniques that are both reasonable and implementable. First, the agent's action space is modeled as a combinatorial action space of trading directions with prespecified trading sizes for each asset . Second, we introduce a mapping function that can replace an initially-determined action that may be infeasible with a feasible action that is reasonably close to the original, ideal action.
After revision:  This study formulates a portfolio trading process as a Markov decision process model for the portfolio trading process, and the model adopts a discrete combinatorial action space of trading directions with prespecified trading sizes for each asset . Second, we introduce a mapping function that can replace an initially-determined action that may be infeasible with a feasible action that is reasonably close to the original, ideal action.