Agent behavior modeling method based on reinforcement learning and human in the loop

Abstract

Computer generated force (CGF) is one of the increasingly important research topics in the field of simulation. However, low modeling efficiency and lack of adaptability are acute problems of traditional CGF modeling. In this study, a method for modeling the agent behavior based on reinforcement learning and human in the loop is proposed to improve the ability and efficiency of agent behavior modeling. First, an overall framework for modeling the behavior of intelligent agents is constructed based on the deep reinforcement learning algorithm Soft Actor Critic (SAC) framework. Second, in order to overcome the slow convergence speed of the SAC framework, a method for human interaction and value evaluation in the loop is introduced, and the specific algorithm flow is designed. Third, in order to verify the performance of the proposed method, experiments are conducted and compared with algorithms using a pure SAC framework based on an example of agent completing specific tasks. Result shows that after 100 episodes of training, the task completion rate of the agent can approach 100% while a pure SAC framework require at least 500 episodes of training to gradually improve the completion rate. Finally, the results demonstrate that the proposed method can significantly improve the efficiency of agent behavior modeling and the task completion rate increases with the number of human interventions in the loop.