arXiv:2510.03013v4 Announce Type: replace Abstract: We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and full distributions of returns. Unlike conventional IRL approaches that recover a deterministic reward estimate or match only expected returns, our method captures richer structure in expert behavior, particularly in learning the reward distribution, by minimizing first-order stochastic dominance (FSD) violations and thus integrating distortion risk measures (DRMs) into policy learning, enabling the r
The continuous advancements in AI research, particularly in addressing complex decision-making under uncertainty, drive the development of more sophisticated reinforcement learning techniques.
This work is important for strategic readers as it enables AI systems to learn more robustly from expert behavior, especially when reward functions or outcomes are inherently uncertain or multi-faceted.
The ability to model full distributions of returns and minimize first-order stochastic dominance violations indicates a move beyond deterministic reward estimates, leading to more resilient and nuanced AI decision-making.
- · AI researchers
- · Robotics
- · Autonomous systems
- · Logistics and supply chain optimization
- · Developers of simplistic IRL techniques
- · Systems highly sensitive to deterministic reward estimation failures
More robust and adaptable AI agents capable of learning complex preferences from expert demonstrations will emerge.
This framework could lead to breakthroughs in areas requiring high-stakes decision-making where uncertainty is paramount, such as financial trading or medical diagnostics.
Widespread adoption could accelerate the development of generalist AI agents by enabling them to better infer nuanced human intent and preferences in diverse, real-world tasks.
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Read at arXiv cs.LG