  In this paper, we present a deep reinforcement learning method for quadcopter
bypassing the obstacle on the flying path. In the past study, the algorithm
only controls the forward direction about quadcopter. In this letter, we use
two functions to control quadcopter. One is quadcopter navigating function. It
is based on calculating coordination point and find the straight path to the
goal. The other function is collision avoidance function. It is implemented by
deep Q-network model. Both two function will output rotating degree, the agent
will combine both output and turn direct. Besides, deep Q-network can also make
quadcopter fly up and down to bypass the obstacle and arrive at the goal. Our
experimental result shows that the collision rate is 14% after 500 flights.
Based on this work, we will train more complex sense and transfer model to the
real quadcopter.
