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Source note2 min read

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

Source

arXiv

arxiv.org/abs/2607.08647v1

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Source

Plain-Language Brief

The research paper proposes a method for teaching machines to learn robust reward functions that can work across various operational contexts. The study focuses on scenarios with diverse feedback modalities and environment dynamics. The proposed hierarchical machine teaching algorithm selects informative environments and strategically queries low-cost feedback within them. The results show that the method outperforms uniform teaching baselines in terms of lower regret and stronger generalization to unseen environments.

What the Document Says

  • The paper discusses a problem where autonomous agents need to behave according to human intent across various operational contexts. It mentions that existing inverse reinforcement learning (IRL) approaches for inferring such objectives focus on single-environment, demonstration-only settings.
  • It is stated that comparing feedback modalities imposes stronger global constraints on rewards in the unlimited-data regime.
  • A hierarchical machine teaching algorithm for reward learning across multiple MDPs (Model of Decision Process) is introduced. The algorithm selects informative environments and queries low-cost feedback within them.
  • The empirical results show that the method achieves lower regret and stronger generalization to unseen environments compared to uniform teaching baselines under identical feedback budgets.

Key Concepts Mentioned

  • autonomous agents
  • reward functions
  • inverse reinforcement learning (IRL)
  • multi-environment
  • demonstration
  • feedback modalities
  • machine teaching
  • MDP

Claims Made by the Document

  • Existing IRL approaches focus on single-environment, demonstration-only settings.
  • Comparing feedback modalities imposes stronger global constraints on rewards in the unlimited-data regime.
  • The proposed method outperforms uniform teaching baselines in terms of lower regret and stronger generalization to unseen environments.

Evidence Used

  • The text mentions existing IRL approaches and their focus on single-environment, demonstration-only settings.
  • It provides a theoretical analysis comparing feedback modalities and shows that comparisons impose stronger global constraints.
  • Empirical results are presented showing the performance of the proposed method compared to uniform teaching baselines.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 heterogeneous feedback modalities and environment dynamics jointly constrain reward functions that generalize across multiple environments. Because demonstrations in one MDP entangle reward information with that environments specific structure, the resulting rewards frequently fail to generalize when the agent is deployed in a new setting. We first analyze how different feedback modalities constrain rewards, showing that, in the unlimited-data regime, comparisons impose strictly stronger global constraints than other modalities. Beyond this theoretical analysis, we introduce a hierarchical machine teaching algorithm for reward learning that operates across multiple MDPs. The algorithm first greedily selects informative environments that expose complementary reward constraints, then strategically queries low-cost feedback within those environments. Empirically, our method achieves substantially lower regret and stronger generalization to held-out environments than uniform teaching baselines under identical feedback budgets, demonstrating the importance of multi-environment, multi-modal teaching for learning dynamics-robust reward functions.