arXiv:2607.08647v1 Announce Type: new Abstract: 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 dynami
The increasing deployment of autonomous agents across diverse operational contexts highlights the immediate need for more robust reward learning that can adapt to varying environments.
Achieving robust reward functions is critical for the reliable and safe deployment of AI systems, especially as they move into complex, real-world scenarios beyond controlled environments.
This research suggests a move towards AI systems that can learn human intent more effectively across different settings, potentially leading to more adaptable and generalizable AI.
- · AI developers
- · Robotics industry
- · Autonomous systems integrators
- · Single-environment AI models
- · Current inverse reinforcement learning limitations
Improved performance and reliability of AI agents in dynamic, real-world situations.
Accelerated adoption of autonomous systems in sectors requiring high adaptability, such as logistics and defense.
Reduced need for extensive re-training or human oversight for AI systems adapting to new operational contexts.
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Read at arXiv cs.LG