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Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow
Frontiers
Oil and gas field development optimization, which involves the determination of the optimal number of wells, their drilling sequence and locations while...
2 months ago
AI Pontryagin or how artificial neural networks learn to control dynamical systems
Nature
We present AI Pontryagin, a versatile control framework based on neural ordinary differential equations that automatically learns control signals.
31 months ago
Paper in Two minutes: Using Mean Field Games for learning behavior policy of large populations
Packt
This ICLR 2018 accepted paper, Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations, deals with inference in models of collective...
78 months ago
Taming hyperparameter tuning in continuous normalizing flows using the JKO scheme
Nature
A normalizing flow (NF) is a mapping that transforms a chosen probability distribution to a normal distribution. Such flows are a common...
17 months ago
Policy Gradients in a Nutshell. Everything you need to know to get… | by Sanyam Kapoor
Towards Data Science
This article aims to provide a concise yet comprehensive introduction to one of the most important class of control algorithms in Reinforcement Learning —...
75 months ago
Self-correcting quantum many-body control using reinforcement learning with tensor networks
Nature
Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert...
13 months ago
Stochastic learning and extremal-field map based autonomous guidance of low-thrust spacecraft
Nature
A supervised stochastic learning method called the Gaussian Process Regression (GPR) is used to design an autonomous guidance law for low-thrust spacecraft.
22 months ago