Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization

no code implementations • 1 Mar 2022 • Vilde B. Gjærum, Inga Strümke, Ole Andreas Alsos, Anastasios M. Lekkas

The main contributions of this work are (1) significantly improving both the accuracy and the build time of a greedy approach for building LMTs by introducing ordering of features in the splitting of the tree, (2) giving an overview of the characteristics of the seafarer/operator and the developer as two different end-users of the agent and receiver of the explanations, and (3) suggesting a visualization of the docking agent, the environment, and the feature attributions given by the LMT for when the developer is the end-user of the system, and another visualization for when the seafarer or operator is the end-user, based on their different characteristics.

Explainable artificial intelligence Reinforcement Learning (RL)