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• W3140147451 startingPage "4552" @default.
• W3140147451 abstract "Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omni-directional target with multiple, homogeneous agents that are subject to unicycle kinematic constraints. We use shared experience to train a policy for a given number of pursuers that is executed independently by each agent at run-time. The training benefits from curriculum learning, a sweeping-angle ordering to locally represent neighboring agents and encouraging good formations with reward structure that combines individual and group rewards. Simulated experiments with a reactive evader and up to eight pursuers show that our learning-based approach, with non-holonomic agents, performs on par with classical algorithms with omni-directional agents, and outperforms their non-holonomic adaptations. The learned policy is successfully transferred to the real world in a proof-of-concept demonstration with three motion-constrained pursuer drones." @default.
• W3140147451 created "2021-04-13" @default.
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• W3140147451 date "2021-07-01" @default.
• W3140147451 modified "2023-10-16" @default.
• W3140147451 title "Decentralized Multi-Agent Pursuit Using Deep Reinforcement Learning" @default.
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• W3140147451 doi "https://doi.org/10.1109/lra.2021.3068952" @default.
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