Reinforcement Learning For Coordination And Control of Swarming Satellites, Phase I

Metadata Updated: February 28, 2019

Inspired by frequent observation of repetitive learned swarm behavior exhibited in nature, this novel program will develop and demonstrate new capabilities in decentralized control of large heterogeneous vehicle swarms limited in communication, sensors, and actuators, with direct application to communication-less coordination. These goals are accomplished through the adaptation and use of Reinforcement Learning solutions to the optimal control problem. Reinforcement Learning approaches define a value function, which represents the total reward for possible actions at a given state, deriving a decentralized formulation for each agent in a Multi-Agent System. The proposal implements the policy gradient method for Reinforcement Learning applied to swarming spacecraft control. Three major tasks are proposed for the development of swarming space vehicle coordination and control: Approximate Optimal Control for Large Swarms, Communication-Less Swarm Coordination Implementation, and Human-Swarm Interactions via Supervised Reinforcement Learning. Algorithm development in Phase I will extend to a Centralized Optimal Control Solution, Inverse Reinforcement Learning for the Local Decentralized Problem, Model Free Learning, "Expert Solution" Conversions to the Local Modified Local Interaction, Inverse Learning for Behavior Determination and Classification, Hyman Designed Dynamic Reward Functions, and Keep Out Zone Models. Follow-on efforts will are proposed for full implementation of the Reinforcement Learning swarm technology for real-time integrated system use and mission integration, including laboratory demonstrations of small robotic units, and the development of flight-qualified software and hardware packages for full integrated technology demonstrations.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_93729
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id c5a68422-3d0b-420f-b06d-1706ea4137e4
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2018-06-01
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash 6cde75712b306002868134162211fc6aff69f8e1
Source Schema Version 1.1

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