Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

2022 ◽  
Author(s):  
Christopher J. Sullivan ◽  
Natasha Bosanac ◽  
Rodney L. Anderson ◽  
Alinda Mashiku
Keyword(s):  
Reinforcement Learning ◽  
Design Space ◽  
Low Thrust ◽  
Multi Objective

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

scholarly journals Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning

2021 ◽  
pp. 1-1
Author(s):  
Stefan Schneider ◽  
Ramin Khalili ◽  
Adnan Manzoor ◽  
Haydar Qarawlus ◽  
Rafael Schellenberg ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Service Coordination ◽  
Multi Objective ◽  
Self Learning

Multi-Objective Design and Optimisation of Bypass Outlet-Guide Vanes

2003 ◽  
Author(s):  
Shahrokh Shahpar ◽  
David Giacche ◽  
Leigh Lapworth
Keyword(s):  
Design Space ◽  
Guide Vane ◽  
Automated Design ◽  
Analysis Procedure ◽  
High Fidelity ◽  
Cfd Analysis ◽  
Optimization System ◽  
Multi Objective ◽  
Cluster Of Workstations ◽  
Stagger Angle

This paper describes the development of an automated design optimization system that makes use of a high fidelity Reynolds-Averaged CFD analysis procedure to minimize the fan forcing and fan BOGV (bypass outlet guide vane) losses simultaneously taking into the account the down-stream pylon and RDF (radial drive fairing) distortions. The design space consists of the OGV’s stagger angle, trailing-edge recambering, axial and circumferential positions leading to a variable pitch optimum design. An advanced optimization system called SOFT (Smart Optimisation for Turbomachinery) was used to integrate a number of pre-processor, simulation and in-house grid generation codes and postprocessor programs. A number of multi-objective, multi-point optimiztion were carried out by SOFT on a cluster of workstations and are reported herein.

Multi-objective optimization and analysis for the design space exploration of analog circuits and solar cells

2017 ◽  
Vol 62 ◽  
pp. 373-383 ◽  
Author(s):  
Andrea Patanè ◽  
Andrea Santoro ◽  
Piero Conca ◽  
Giovanni Carapezza ◽  
Antonino La Magna ◽  
...  
Keyword(s):  
Solar Cells ◽  
Analog Circuits ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Multi Objective Optimization ◽  
Multi Objective
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