arXiv:2606.18531v1 Announce Type: cross Abstract: Offline reinforcement learning is typically analyzed under process-level reward supervision, yet many sequential decision datasets record only trajectory-level outcomes. We develop a statistical theory for offline policy optimization from such outcome-level supervision. We first study the canonical setting where the target remains the expected cumulative reward, but each offline trajectory provides only a scalar label whose conditional mean is the cumulative return. We propose OPAC, a pessimistic actor-critic algorithm that learns a latent rewa
The paper addresses a current challenge in offline reinforcement learning where available data often has coarse-grained supervision, reflecting ongoing efforts to improve AI efficiency and data utilization.
Improving offline reinforcement learning from trajectory-level supervision enhances the ability to train AI models with less precise real-world data, expanding the applications and efficiency of AI agents.
This research provides a theoretical framework and an algorithm (OPAC) to efficiently learn from less granular data, potentially reducing the need for costly fine-grained reward engineering in real-world AI deployments.
- · AI researchers
- · Companies with limited granular data
- · SaaS companies leveraging AI
- · Robotics
More robust and generalizable offline RL algorithms can be developed and applied to real-world datasets.
Increased adoption of offline RL in complex domains where process-level reward supervision is impractical or unavailable.
Acceleration in the development and deployment of AI agents in scenarios previously limited by data annotation challenges.
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Read at arXiv cs.LG