Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback
arXiv:2606.01081v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictive models by optimizing downstream decision quality rather than standalone prediction accuracy. For contextual linear optimization, most existing DFL methods assume offline data and full observations of the objective cost vector. We develop an on-policy learning method for sequential contextual linear optimization under partial feedback, generalizing the standard bandit feedback setting. Our method learns a stochastic predict-then-optimize policy that samples a cost-vector prediction from a condition
This development in decision-focused learning emerges as AI research pushes for more robust and real-world applicable autonomous systems, moving beyond purely predictive models.
Improved decision quality in contextual linear optimization under partial feedback can significantly enhance the effectiveness and efficiency of AI agents operating in dynamic, uncertain environments.
The shift from optimizing for prediction accuracy to optimizing for downstream decision quality could lead to more effective and reliable AI deployments in complex, real-world scenarios.
- · AI agents developers
- · Logistics and supply chain optimization
- · Real-time decision systems
- · Reinforcement learning applications
- · Systems relying solely on prediction accuracy metrics
- · Legacy optimization approaches
- · Industries slow to adopt advanced AI optimization
More efficient and adaptable AI-driven decision-making processes become feasible across various industries.
This could accelerate the deployment of autonomous systems in sectors like financial trading, resource management, and complex manufacturing.
General improvements in AI decision-making could further collapse white-collar workflows by enabling more sophisticated agentic systems.
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Read at arXiv cs.LG