Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning

As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such changes rather than overfitting to any single environment. Inverse reinforcement learning (IRL) provides a principled way to infer such objectives from human feedback. However, existing analyses of optimal teaching approaches for IRL focus on single-environment, demonstration-only settings, leaving underexplored how hete

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ethics.ai (9 July 2026), “Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning,” evidence record 82, https://ethics.ai/record/82 (originally published by arXiv).

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