Abstract: A deep reinforcement learning (DRL) intelligent decision-making platform based on a unified AI framework includes a parameter configuration module, a general-purpose module, an original environment module, an environment vectorization module, an environments maker, a mathematical utilities module, a model library and a runner. Parameters of a DRL model are selected through the parameter configuration module, and read by the general-purpose module. Based on the read parameters, a representer, a policy module, a learner, and an intelligent agent are called from the model library and created, where necessary function definitions and optimizers are called from the mathematical utilities module. Based on the read parameters, the environment vectorization module is created based on the original environment. The intelligent agent and environments are input into the runner to compute an action output, which executes the action output to realize the intelligent decision making.