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Scaling Multi-agent Learning in Complex Environments
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Cooperative multi-agent systems (MAS) are finding applications in a wide variety of domains, including sensor networks, robotics, distributed control, collabora
Transfer Learning for Multiagent Reinforcement Learning Systems
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Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to
Coordination of Large-Scale Multiagent Systems
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Challenges arise when the size of a group of cooperating agents is scaled to hundreds or thousands of members. In domains such as space exploration, military an
Scaling Reinforcement Learning to the Unconstrained Multi-agent Domain
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Reinforcement learning is a machine learning technique designed to mimic the way animals learn by receiving rewards and punishment. It is designed to train inte
Cooperation and Communication in Multiagent Deep Reinforcement Learning
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Reinforcement learning is the area of machine learning concerned with learning which actions to execute in an unknown environment in order to maximize cumulativ