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Learning Interaction Kernels for Agent Systems on Riemannian Manifolds
Interacting agent and particle systems are extensively used to model complex phenomena in science and engineering.
Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm
We analyze the results and modify the parameters to approach a well-behaved strategy for our agents.
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