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• W2891663723 abstract "Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment. We focus on a multiagent planning model subclass, relevant to urban settings, where agent interactions are dependent on their ``collective influence'' on each other, rather than their identities. Unlike previous work, we address a general setting where system reward is not decomposable among agents. We develop collective actor-critic RL approaches for this setting, and address the problem of multiagent credit assignment, and computing low variance policy gradient estimates that result in faster convergence to high quality solutions. We also develop difference rewards based credit assignment methods for the collective setting. Empirically our new approaches provide significantly better solutions than previous methods in the presence of global rewards on two real world problems modeling taxi fleet optimization and multiagent patrolling, and a synthetic grid navigation domain." @default.
• W2891663723 created "2018-09-27" @default.
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• W2891663723 creator A5051203367 @default.
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• W2891663723 date "2018-01-01" @default.
• W2891663723 modified "2023-09-23" @default.
• W2891663723 title "Credit Assignment For Collective Multiagent RL With Global Rewards" @default.
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• W2891663723 hasPublicationYear "2018" @default.
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