Application of Deep Reinforcement Learning in Mobile Robot Path Planning

In order to make the robot obtain the optimal action directly from the original visual perception without any hand-crafted features and features matching, a novel end-to-end path planning method--mobile robot path planning using deep reinforcement learning is proposed. Firstly, a deep Q-nehvork (DQN) is designed and trained to approximate the mobile robot state-action value function. Then, the Q value corresponding to each possible mobile robot action (i.e., turn left, turn right, forward) is determined by the well trained DQN, here, the input of the DQN is the original RGB image (image pixels) captured from the environment without any hand-crafted features and features matching; Finally, the current optimal mobile robot action is selected by the action selection strategy. Mobile robot reach to the goal point while avoiding obstacles ultimately. 30 times path planning experiments are conducted in the seekavoid_arena_01 environment on DeepMind Lab platform. The experimental results show that our deep reinforcement learning based robot path planning method is an effective end-to end mobile robot path planning method.