Self-model acquisition and target explanation of a deep reinforcement learning agent

Abstract

In teamwork, communication has an important role in understanding co-worker's future behavior. In this paper, we propose a method for a machine learning agent to explain the target of the agent's own actions. The agent grasps the target of its own actions by predicting the result of the actions, and gives an appropriate expression for the predicted results by estimating the meanings of expressions on the basis of the shared reward.