Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Collectives and Design Complex Systems
Collectives and Design Complex Systems
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In the near future, there is potential for a tremendous expansion in the number of Earth-orbiting CubeSats, due to reduced cost associated with platform standardization, availability of standardized parts for CubeSats, and reduced launching costs due to improved packaging methods and lower cost launchers. However, software algorithms capable of efficiently coordinating CubeSats have not kept up with their hardware gains, making it likely that these CubSats will be severely underutilized. Fortunately, these coordination issues can be addressed with multiagent algorithms. In this paper, we show how a multiagent system can be used to address the particular problem of how a third party should bid for use of existing Earth-observing CubeSats so that it can achieve optical coverage over a key geographic region of interest. In this model, an agent is assigned to every CubeSat from which observations may be purchased, and agents must decide how much to offer for these services. We address this problem by having agents use reinforcement learning algorithms with agent-specific shaped rewards. The results show an eight fold improvement over a simple strawman allocation algorithm and a two fold improvement over a multiagent system using standard reward functions.