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Space debris removal: Learning to cooperate and the price of anarchy

Space debris removal: Learning to cooperate and the price of anarchy
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 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.
Klima, Richard
f33da4fc-f05a-4b5f-b73c-0eb0e8190670
Bloembargen, Daan
04c8ed31-52da-45f9-8244-3d7874f3ec8a
Savani, Rahul
58baa31a-7331-4dad-b3cd-a17d7b5d7d42
Tuyls, Karl
2dc789c5-88de-4749-8159-fa50fb9228a4
Wittig, Alexander
3a140128-b118-4b8c-9856-a0d4f390b201
Sapera, Andrei
a07b7dc7-d106-49db-b85a-33375ccdf993
Izzo, Dario
89b8b40f-3786-469f-bb69-795c4d845a0e
Klima, Richard
f33da4fc-f05a-4b5f-b73c-0eb0e8190670
Bloembargen, Daan
04c8ed31-52da-45f9-8244-3d7874f3ec8a
Savani, Rahul
58baa31a-7331-4dad-b3cd-a17d7b5d7d42
Tuyls, Karl
2dc789c5-88de-4749-8159-fa50fb9228a4
Wittig, Alexander
3a140128-b118-4b8c-9856-a0d4f390b201
Sapera, Andrei
a07b7dc7-d106-49db-b85a-33375ccdf993
Izzo, Dario
89b8b40f-3786-469f-bb69-795c4d845a0e

Klima, Richard, Bloembargen, Daan, Savani, Rahul, Tuyls, Karl, Wittig, Alexander, Sapera, Andrei and Izzo, Dario (2018) Space debris removal: Learning to cooperate and the price of anarchy. Frontiers in Robotics and AI, 5 (54). (doi:10.3389/frobt.2018.00054).

Record type: Article

Abstract

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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More information

Accepted/In Press date: 18 April 2018
e-pub ahead of print date: 4 June 2018

Identifiers

Local EPrints ID: 420327
URI: http://eprints.soton.ac.uk/id/eprint/420327
PURE UUID: 0da84251-7f93-4f08-a4e0-b1e54f439b6d
ORCID for Alexander Wittig: ORCID iD orcid.org/0000-0002-4594-0368

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Date deposited: 04 May 2018 16:30
Last modified: 16 Mar 2024 06:34

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Contributors

Author: Richard Klima
Author: Daan Bloembargen
Author: Rahul Savani
Author: Karl Tuyls
Author: Andrei Sapera
Author: Dario Izzo

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