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

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new hierarchical machine teaching algorithm, Hierarchical Set Cover Optimal Teaching (HSCOT), addresses the challenge of learning robust reward functions for autonomous agents deployed across diverse environments. Traditional inverse reinforcement learning (IRL) teaching methods often overfit to single environments and rely solely on demonstrations. This work analyzes how different feedback modalities constrain rewards, finding that comparisons offer stronger global constraints in unlimited-data scenarios, while demonstrations are more efficient under limited budgets. HSCOT operates across multiple Markov Decision Processes (MDPs), first selecting informative environments that reveal complementary reward constraints, then strategically querying low-cost feedback. Empirically, HSCOT demonstrates substantially lower regret and superior generalization to held-out environments compared to uniform teaching baselines, highlighting the importance of multi-environment, multi-modal teaching for dynamics-robust reward learning.

Key takeaway

For Machine Learning Engineers developing autonomous agents for varied operational contexts, relying solely on single-environment, demonstration-based reward learning risks poor generalization. You should adopt multi-environment machine teaching frameworks like HSCOT to explicitly account for environment dynamics and utilize diverse feedback modalities. Strategically selecting environments and feedback types, such as using demonstrations for tight budgets and comparisons for extensive data, will significantly improve reward function robustness and reduce regret in new deployment settings.

Key insights

Multi-modal, multi-environment machine teaching is crucial for learning reward functions that generalize across diverse operational contexts.

Principles

• Reward identifiability is environment-dependent.
• Comparisons impose stronger global constraints in unlimited-data regimes.
• Demonstrations are more constraint-efficient under limited feedback budgets.

Method

HSCOT hierarchically selects informative environments to expose complementary reward constraints, then strategically queries low-cost feedback within them.

In practice

• Combine diverse feedback types for robust reward learning.
• Prioritize environment selection before feedback queries.
• Use demonstrations for tight budgets, comparisons for extensive data.

Topics

• Machine Teaching
• Reward Learning
• Inverse Reinforcement Learning
• Multi-Modal Feedback
• Multi-Environment Learning
• Autonomous Agents

Code references

• Alilarian/multienv-reward-teaching
• Alilarian/multienv-reward-teaching1

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.