arXiv AI
Efficient Geothermal Well-Control Optimization via Diffusion-Surrogate Reinforcement Learning
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arXiv Machine Learning
Jun 17
Decision-Driven Geosteering Under Uncertainty: A Unified Framework for Sequential Decision Optimization
arXiv:2606. 17331v1 Announce Type: new Abstract: Geosteering requires navigating a well trajectory through an unknown geological configuration, while sequentially updating decisions based on indirect measurements acquired during drilling.
arXiv Machine Learning
Jun 26
Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation
arXiv:2605. 02405v2 Announce Type: replace Abstract: Closed-loop management of geological CO2 storage requires control policies that adapt to uncertain reservoir behavior while relying on observations that are realistically available during operation.
arXiv AI
3d ago
Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning
The paper introduces Diffusion-Augmented Markov Decision Processes (DA‑MDPs), a framework that extends Maximum Entropy Reinforcement Learning to diffusion-based policies. DA‑MDPs treat each reverse‑diffusion step as an RL decision, deriving a tractable reverse‑KL bound that decomposes across denoising transitions and yields diffusion‑augmented soft rewards, value functions, and policy objectives. The authors implement this framework with PPO, REPPO, and a maximum‑entropy WPO variant, showing improved continuous‑control performance, higher success rates on manipulation tasks, and memory‑efficient training with action chunking.
arXiv Machine Learning
Jun 25
A Zeroth-Order Deep Learning Method for Fully Nonlinear Parabolic Partial Differential Equations with Unknown Coefficients
arXiv:2606. 24999v1 Announce Type: new Abstract: High-dimensional partial differential equations (PDEs) with unknown coefficients arise widely in scientific machine learning, including continuous-time reinforcement learning, yet solving them efficiently in a data-driven way remains challenging.
arXiv Machine Learning
Jun 16
CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimization
arXiv:2602. 08210v2 Announce Type: replace Abstract: Heatmap-based solvers have emerged as a promising paradigm for Combinatorial Optimization (CO).
arXiv Machine Learning
Jun 10
MODIP: Efficient Model-Based Optimization for Diffusion Policies
arXiv:2606. 10825v1 Announce Type: new Abstract: Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC).