Discrete Geometry for Computer Imagery

Debled-Rennesson, Isabelle; Domenjoud, Éric; Kerautret, Bertrand; Even, Philippe

doi:10.1016/j.dam.2013.03.026

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2020

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“…In practice we use the Delaunay Triangulation [4] to get a navigation mesh on the map and then navigate the agents with A * algorithm. The exact process of A * algorithm could be seen in [2].…”

Section: Pathfinding Methods Mmentioning

confidence: 99%

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Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm

Qiu¹

2020

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We develop a new framework for multi-agent collision avoidance problem. The framework combined traditional pathfinding algorithm and reinforcement learning. In our approach, the agents learn whether to be navigated or to take simple actions to avoid their partners via a deep neural network trained by reinforcement learning at each time step. This framework makes it possible for agents to arrive terminal points in abstract new scenarios.In our experiments, we use Unity3D and Tensorflow to build the model and environment for our scenarios. We analyze the results and modify the parameters to approach a well-behaved strategy for our agents. Our strategy could be attached in different environments under different cases, especially when the scale is large.

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Section: Pathfinding Methods Mmentioning

confidence: 99%

“…Several methods have been developed to solve this problem. One method related to our work is to use triangular mesh [4] instead of grid world to decrease the computational cost [5]. Compared to continuous methods, discretized methods typically require smaller computational cost and have higher efficiency.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm

Qiu¹

2020

Preprint

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Discrete Geometry for Computer Imagery

Abstract: International audienc

Cited by 1 publication

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Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm

Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm

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