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

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