Interpreting Neural Policies with Disentangled Tree Representations
This work addresses interpretability for safety-critical robots, but it is incremental as it builds on existing disentanglement methods.
The paper tackles the problem of interpreting neural policies in robot learning by using disentangled tree representations to identify explanatory factors like skills or behaviors, and introduces metrics to measure disentanglement, showing effectiveness through extensive experiments.
The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how learning-based controllers make decisions is crucial since robots are often safety-critical systems. This urges a formal and quantitative understanding of the explanatory factors in the interpretability of robot learning. In this paper, we aim to study interpretability of compact neural policies through the lens of disentangled representation. We leverage decision trees to obtain factors of variation [1] for disentanglement in robot learning; these encapsulate skills, behaviors, or strategies toward solving tasks. To assess how well networks uncover the underlying task dynamics, we introduce interpretability metrics that measure disentanglement of learned neural dynamics from a concentration of decisions, mutual information and modularity perspective. We showcase the effectiveness of the connection between interpretability and disentanglement consistently across extensive experimental analysis.