A Robot Exploration Strategy Based on Q learning Network
This paper introduces a reinforcement learning method for exploring a corridor environment with the depth information from an RGB D sensor only. The robot controller achieves obstacle avoidance ability by pre training of feature maps using the depth information. The system is based on the recent Deep Q Network (DQN) framework where a convolution neural network structure was adopted in the Q value estimation of the Q learning method. We separate the DQN into a supervised deep learning structure and a Q learning network. The experiments of a Turtlebot in the Gazebo simulation environment show the robustness to different kinds of corridor environments. All of the experiments use the same pre training deep learning structure. Note that the robot is traveling in environments which are different from the pre training environment. It is the first time that raw sensor information is used to build such an exploring strategy for robotics by reinforcement learning.