A parallel deep Q-network (DQN) algorithm is presented for solving multiagent constrained formation path planning, where reaching destination, avoiding obstacles, and maintaining formation are simultaneously considered as independent or interactive tasks. Parallel Q-networks are utilized for each agent to sense different feature information and learn independent behavior policy. Comprehensive reward function is designed in consideration of respective requirements and interaction constraints to correctly guide the training. In order to demonstrate the effectiveness of the algorithm, we build an end-to-end model by designing a pixel game. Both training and testing are carried out in the game with double dueling DQN and the results show that the parallel deep Q-network path planner eventually complete the three tasks very well.