Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : June 05, 2019 - June 08, 2019
It is desirable for a robot to be able to accomplish assigned tasks even in a changing environment. Deep reinforcement learning draws attention recently as a learning method for autonomous mobile robots. This research picks up an example mission where an experimental robot TurtleBot3 Burger reaches the target point avoiding obstacles. Simulation experiments are performed to compare conventional Q-learning and DQN．As a result, DQN is superior to Q-learning in that it can effectively learn the proper actions using the sensor output directly as the input.
