Researchers have developed a new approach to decision-focused learning (DFL) for Markov decision processes (MDPs) that addresses scalability limitations of existing methods. By reformulating the MDP as an occupancy measure-based linear program (LP), the new technique derives a closed-form gradient and uses an augmented Lagrangian surrogate with random row sketching to handle discontinuous gradients and large state spaces. This method achieves lower regret and significantly reduced computation costs compared to previous KKT-based DFL and two-stage baselines across various tasks. AI
IMPACT This research could enable more scalable and efficient decision-making in complex AI systems by improving learning algorithms for sequential decision problems.
RANK_REASON The item is an academic paper detailing a new method for decision-focused learning in Markov decision processes. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- augmented Lagrangian surrogate
- Bellman equation
- Decision-Focused Learning
- Karush–Kuhn–Tucker conditions
- linear programming
- Markov decision process
- occupancy measure
- random row sketching
- soft state-aggregation
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