Dynamic policy adaptation for inference control of queries to a propositional information system1

2012 ◽  
Vol 20 (5) ◽  
pp. 509-546 ◽  
Author(s):  
Joachim Biskup
Robotica ◽  
2021 ◽  
pp. 1-31
Author(s):  
Andrew Spielberg ◽  
Tao Du ◽  
Yuanming Hu ◽  
Daniela Rus ◽  
Wojciech Matusik

Abstract We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.


2020 ◽  
pp. 1-15
Author(s):  
Tristan Cazenave ◽  
Jean-Yves Lucas ◽  
Thomas Triboulet ◽  
Hyoseok Kim

Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm that learns a playout policy in order to solve a single player game. In this paper we apply NRPA to the vehicle routing problem. This problem is important for large companies that have to manage a fleet of vehicles on a daily basis. Real problems are often too large to be solved exactly. The algorithm is applied to standard problem of the literature and to the specific problems of EDF (Electricité De France, the main French electric utility company). These specific problems have peculiar constraints. NRPA gives better result than the algorithm previously used by EDF.


Author(s):  
Germán Gieczewski ◽  
Christopher Li
Keyword(s):  

Author(s):  
Ronny Bianchi ◽  
Aldo Enrietti ◽  
Renato Lanzetti
Keyword(s):  

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