MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network

• URL: http://arxiv.org/abs/2001.09001v2
• Date: Tue, 3 Mar 2020 21:17:45 GMT
• Title: MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network
• Authors: Priyabrata Saha, Arslan Ali, Burhan A. Mudassar, Yun Long, and Saibal Mukhopadhyay
• Abstract summary: MagNet is a neural network-based multi-agent interaction model. It is trained to discover the core dynamics of a multi-agent system from observations. It is tuned on-line to learn agent-specific parameters to ensure accurate prediction.
• Score: 11.285833408524708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observations. We formulate a multi-agent system as a coupled non-linear network with a generic ordinary differential equation (ODE) based state evolution, and develop a neural network-based realization of its time-discretized model. MagNet is trained to discover the core dynamics of a multi-agent system from observations, and tuned on-line to learn agent-specific parameters of the dynamics to ensure accurate prediction even when physical or relational attributes of agents, or number of agents change. We evaluate MagNet on a point-mass system in two-dimensional space, Kuramoto phase synchronization dynamics and predator-swarm interaction dynamics demonstrating orders of magnitude improvement in prediction accuracy over traditional deep learning models.

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