Path Planning Technology of Unmanned Vehicle Based on Improved Deep Reinforcement Learning

As the basic problem of unmanned vehicle navigation control, path planning has been widely studied. Reinforcement learning (RL) has been found an effective way of path optimization for the highly nonlinear and unmodeled dynamics. However, the RL based methods suffer from the "dimension disaster" under the high-dimension state spaces. In this paper, the path planning of an unmanned vehicle with collision avoidance is considered, and an improved Deep Q-Network (DQN) algorithm is proposed to reduce the computation load in the high-dimension state space. First, the states, actions and rewards are determined based on the task requirement, and a smoothing function is defined as an additional penalty term to modify the basic reward function. Then, the two-dimension grid of the state space is mapped to a gray image, which is applied as the input of a neural network, i.e., the Q-Network. Finally, simulation results show that the modified DQN algorithm is more stable and the fluctuation frequency is significantly reduced.