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Source note3 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 researchers introduce a machine teaching algorithm that operates across multiple MDPs (multi-agent decision making problems) to learn robust reward functions from heterogeneous feedback modalities in diverse operational contexts, addressing the issue of overfitting to single environments. The new method achieves lower regret and stronger generalization compared to uniform teaching baselines.

What the Document Says

  • The paper discusses a problem where autonomous agents need rewards that are robust across different operational contexts.
  • Inverse reinforcement learning (IRL) is mentioned as a solution for inferring such objectives from human feedback, but the authors claim existing analyses focus on single-environment settings.
  • The text introduces a problem with current IRL methods: rewards learned from demonstrations in one MDP often fail to generalize when deployed in a new setting due to entanglement of reward information with environment-specific structure.
  • A theoretical analysis is provided showing that comparisons impose stronger global constraints on rewards compared to other feedback modalities in the unlimited-data regime.
  • The authors introduce a hierarchical machine teaching algorithm for learning dynamics-robust reward functions across multiple MDPs.
  • The algorithm selects informative environments and strategically queries low-cost feedback within those environments.
  • Empirical results show that the new method achieves lower regret and stronger generalization to held-out environments than uniform teaching baselines under identical feedback budgets.

Key Concepts Mentioned

  • autonomous agents
  • reward functions
  • inverse reinforcement learning (IRL)
  • multi-agent decision making problems (MDPs)
  • diverse operational contexts
  • heterogeneous feedback modalities
  • environment dynamics
  • regret
  • generalization

Claims Made by the Document

  • Existing IRL analyses focus on single-environment settings.
  • Rewards learned from demonstrations in one MDP often fail to generalize when deployed in a new setting.
  • Comparisons impose stronger global constraints on rewards compared to other feedback modalities in the unlimited-data regime.
  • The hierarchical machine teaching algorithm achieves lower regret and stronger generalization compared to uniform teaching baselines.

Evidence Used

  • The authors mention that existing analyses of optimal teaching approaches for IRL focus on single-environment, demonstration-only settings.
  • The text provides an example of rewards learned from demonstrations failing to generalize when deployed in a new setting.
  • The theoretical analysis presented shows that comparisons impose stronger global constraints on rewards.
  • Empirical results showing lower regret and stronger generalization are presented.

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.