  Portfolio management is the art and science in fiance that concerns
continuous reallocation of funds and assets across financial instruments to
meet the desired returns to risk profile. Deep reinforcement learning (RL) has
gained increasing interest in portfolio management, where RL agents are trained
base on financial data to optimize the asset reallocation process. Though there
are prior efforts in trying to combine RL and portfolio management, previous
works did not consider practical aspects such as transaction costs or short
selling restrictions, limiting their applicability. To address these
limitations, we propose a general RL framework for asset management that
enables continuous asset weights, short selling and making decisions with
relevant features. We compare the performance of three different RL algorithms:
Policy Gradient with Actor-Critic (PGAC), Proximal Policy Optimization (PPO),
and Evolution Strategies (ES) and demonstrate their advantages in a simulated
environment with transaction costs. Our work aims to provide more options for
utilizing RL frameworks in real-life asset management scenarios and can benefit
further research in financial applications.
