Reward Learning with Trees: Methods and Evaluation

Tom Bewley, Jonathan Lawry, Arthur Richards, Rachel Craddock, Ian Henderson

Keywords: reinforcement learning, reward learning, alignment, human-agent interaction, explainable AI, XAI, interpretability, decision trees

TL;DR: We show that reward learning with tree models can be competitive with neural networks, and demonstrate some of its interpretability benefits.

Abstract: Recent efforts to learn reward functions from human feedback have tended to use deep neural networks, whose lack of transparency hampers our ability to explain agent behaviour or verify alignment. We explore the merits of learning intrinsically interpretable tree models instead. We develop a recently proposed method for learning reward trees from preference labels, and show it to be broadly competitive with neural networks on challenging high-dimensional tasks, with good robustness to limited or corrupted data. Having found that reward tree learning can be done effectively in complex settings, we then consider why it should be used, demonstrating that the interpretable reward structure gives significant scope for traceability, verification and explanation.

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