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12 April 2021 Multi-agent reinforcement learning for convex optimization
Amir Morcos, Aaron West, Brian Maguire
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Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 1174618 (2021) https://doi.org/10.1117/12.2585624
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
Multi-Agent Reinforcement Learning (RL) algorithms have recently demonstrated their ability to discover/develop new strategies to solving problems. This work aims to apply this same methodology to solving convex optimization problems in an attempt to train agents to outperform popular optimizers. This work will examine methods to frame the problem to the RL agents which encourage rapid convergence. Additionally we will discuss the various ways to represent the environment or state that the agents will operate in, as well as the action space and rewards.
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Amir Morcos, Aaron West, and Brian Maguire "Multi-agent reinforcement learning for convex optimization", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 1174618 (12 April 2021); https://doi.org/10.1117/12.2585624
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KEYWORDS
Convex optimization
Evolutionary algorithms
Machine learning
Artificial intelligence
Basic research
Space operations
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