Maximum Entropy Semi-Supervised Inverse Reinforcement Learning

2026-04-23 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

This paper introduces Maximum Entropy Semi-Supervised Inverse Reinforcement Learning (MESSI), an algorithm that enhances MaxEnt-IRL by incorporating unsupervised trajectories. MaxEnt-IRL addresses the ambiguity in apprenticeship learning by using the maximum entropy principle to find reward functions that match an expert's behavior. MESSI extends this by integrating unsupervised data through a pairwise penalty on trajectories, which encourages similar rewards for intrinsically similar trajectories. The algorithm solves an optimization problem that balances the log-likelihood of expert trajectories with this coherence penalty. Empirical results from highway driving and grid-world problems demonstrate that MESSI effectively utilizes unsupervised data to improve performance over standard MaxEnt-IRL, especially when unsupervised trajectories provide relevant information about the problem's structure. The method also introduces numerical stability improvements to MaxEnt-IRL, such as feature normalization and a reward vector constraint $\theta_{\max}$.

Key takeaway

For research scientists developing Inverse Reinforcement Learning (IRL) systems, consider implementing MESSI to improve reward function learning, especially when limited expert demonstrations are available. By incorporating unsupervised trajectories and a pairwise similarity penalty, you can achieve more robust and accurate models. Pay close attention to the choice of similarity function and the $\lambda$ regularization parameter, as their proper tuning is critical for MESSI to outperform traditional MaxEnt-IRL and avoid performance degradation.

Key insights

MESSI improves Inverse Reinforcement Learning by using unsupervised data to refine reward functions via a pairwise similarity penalty.

Principles

• Integrate unsupervised data to enhance expert-driven learning.
• Penalize reward vectors that assign different rewards to similar trajectories.
• Ensure local consistency of trajectory probabilities.

Method

MESSI optimizes a reward vector by maximizing expert trajectory likelihood while applying a pairwise penalty to enforce similar rewards for similar trajectories, using gradient descent with feature normalization and a reward vector constraint.

In practice

• Use RBF kernel for generic trajectory similarity.
• Normalize features to stabilize reward function learning.
• Constrain reward vector magnitude to prevent numerical instability.

Topics

• Inverse Reinforcement Learning
• Maximum Entropy IRL
• Semi-Supervised Learning
• MESSI Algorithm
• Apprenticeship Learning

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

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