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Space Debris Removal: Learning to Cooperate and the Price of Anarchy

In this paper we study space debris removal from a game-theoretic perspective. In particular we focus on the question whether and how self-interested agents can cooperate in this dilemma, which resembles a tragedy of the commons scenario. We compare centralised and decentralised solutions and the co...

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Autores principales: Klima, Richard, Bloembergen, Daan, Savani, Rahul, Tuyls, Karl, Wittig, Alexander, Sapera, Andrei, Izzo, Dario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806007/
https://www.ncbi.nlm.nih.gov/pubmed/33500936
http://dx.doi.org/10.3389/frobt.2018.00054
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author Klima, Richard
Bloembergen, Daan
Savani, Rahul
Tuyls, Karl
Wittig, Alexander
Sapera, Andrei
Izzo, Dario
author_facet Klima, Richard
Bloembergen, Daan
Savani, Rahul
Tuyls, Karl
Wittig, Alexander
Sapera, Andrei
Izzo, Dario
author_sort Klima, Richard
collection PubMed
description In this paper we study space debris removal from a game-theoretic perspective. In particular we focus on the question whether and how self-interested agents can cooperate in this dilemma, which resembles a tragedy of the commons scenario. We compare centralised and decentralised solutions and the corresponding price of anarchy, which measures the extent to which competition approximates cooperation. In addition we investigate whether agents can learn optimal strategies by reinforcement learning. To this end, we improve on an existing high fidelity orbital simulator, and use this simulator to obtain a computationally efficient surrogate model that can be used for our subsequent game-theoretic analysis. We study both single- and multi-agent approaches using stochastic (Markov) games and reinforcement learning. The main finding is that the cost of a decentralised, competitive solution can be significant, which should be taken into consideration when forming debris removal strategies.
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spelling pubmed-78060072021-01-25 Space Debris Removal: Learning to Cooperate and the Price of Anarchy Klima, Richard Bloembergen, Daan Savani, Rahul Tuyls, Karl Wittig, Alexander Sapera, Andrei Izzo, Dario Front Robot AI Robotics and AI In this paper we study space debris removal from a game-theoretic perspective. In particular we focus on the question whether and how self-interested agents can cooperate in this dilemma, which resembles a tragedy of the commons scenario. We compare centralised and decentralised solutions and the corresponding price of anarchy, which measures the extent to which competition approximates cooperation. In addition we investigate whether agents can learn optimal strategies by reinforcement learning. To this end, we improve on an existing high fidelity orbital simulator, and use this simulator to obtain a computationally efficient surrogate model that can be used for our subsequent game-theoretic analysis. We study both single- and multi-agent approaches using stochastic (Markov) games and reinforcement learning. The main finding is that the cost of a decentralised, competitive solution can be significant, which should be taken into consideration when forming debris removal strategies. Frontiers Media S.A. 2018-06-04 /pmc/articles/PMC7806007/ /pubmed/33500936 http://dx.doi.org/10.3389/frobt.2018.00054 Text en Copyright © 2018 Klima, Bloembergen, Savani, Tuyls, Wittig, Sapera and Izzo http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Klima, Richard
Bloembergen, Daan
Savani, Rahul
Tuyls, Karl
Wittig, Alexander
Sapera, Andrei
Izzo, Dario
Space Debris Removal: Learning to Cooperate and the Price of Anarchy
title Space Debris Removal: Learning to Cooperate and the Price of Anarchy
title_full Space Debris Removal: Learning to Cooperate and the Price of Anarchy
title_fullStr Space Debris Removal: Learning to Cooperate and the Price of Anarchy
title_full_unstemmed Space Debris Removal: Learning to Cooperate and the Price of Anarchy
title_short Space Debris Removal: Learning to Cooperate and the Price of Anarchy
title_sort space debris removal: learning to cooperate and the price of anarchy
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806007/
https://www.ncbi.nlm.nih.gov/pubmed/33500936
http://dx.doi.org/10.3389/frobt.2018.00054
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