  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.
