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The importance of explainability in AI has become a pressing concern, forwhich several explainable AI (XAI) approaches have been recently proposed.However, most of the available XAI techniques are post-hoc methods, whichhowever may be only partially reliable, as they do not reflect exactly thestate of the original models. Thus, a more direct way for achieving XAI isthrough interpretable (also called glass-box) models. These models have beenshown to obtain comparable (and, in some cases, better) performance withrespect to black-boxes models in various tasks such as classification andreinforcement learning. However, they struggle when working with raw data,especially when the input dimensionality increases and the raw inputs alone donot give valuable insights on the decision-making process. Here, we propose touse end-to-end pipelines composed of multiple interpretable models co-optimizedby means of evolutionary algorithms, that allows us to decompose thedecision-making process into two parts: computing high-level features from rawdata, and reasoning on the extracted high-level features. We test our approachin reinforcement learning environments from the Atari benchmark, where weobtain comparable results (with respect to black-box approaches) in settingswithout stochastic frame-skipping, while performance degrades in frame-skippingsettings.

Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs