Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
arXiv:2605.30903v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) typically assumes demonstrations from a single optimal demonstrator, but in many applications data come from multiple imperfect demonstrators with heterogeneous suboptimality levels. We study reward learning in this setting through a feasible-reward-set framework: for each demonstrator, we encode its declared suboptimality level as a linear constraint and intersect the resulting feasible sets across demonstrators. Our theoretical analysis shows that the joint feasible set shrinks monotonically as data are adde
This research addresses a practical limitation in Inverse Reinforcement Learning (IRL), a core AI technique, at a time when AI systems are increasingly deployed in real-world scenarios with imperfect data sources.
Improved IRL methods, especially with real-world data imperfections, are critical for developing more robust and adaptable AI agents capable of learning from diverse and less-than-optimal human demonstrations.
This approach allows IRL to effectively learn from suboptimal demonstrators, broadening its applicability in practical settings where optimal demonstrations are rare or impossible.
- · AI developers
- · Robotics
- · Autonomous systems
- · AI systems relying solely on optimal demonstration
More resilient and versatile AI models will emerge that can learn effectively from varied human behavior.
This could accelerate the development and deployment of AI agents in complex environments where expert-level demonstrations are not always available.
The ability of AI to learn from 'good enough' demonstrations might lower barriers to entry for AI development, expanding its reach into new domains.
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Read at arXiv cs.LG