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

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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Identifiers

ORCID for Alexander Wittig: orcid.org/0000-0002-4594-0368

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