Özet:
Deep reinforcement learning (DRL) exhibits a
promising approach for controlling humanoid robot
locomotion. However, only values relating sensors such as IMU,
gyroscope, and GPS are not sufficient robots to learn their
locomotion skills. In this article, we aim to show the success of
vision based DRL. We propose a new vision based deep
reinforcement learning algorithm for the locomotion of the
Robotis-op2 humanoid robot for the first time. In experimental
setup, we construct the locomotion of humanoid robot in a
specific environment in the Webots software. We use Double
Dueling Q Networks (D3QN) and Deep Q Networks (DQN) that
are a kind of reinforcement learning algorithm. We present the
performance of vision based DRL algorithm on a locomotion
experiment. The experimental results show that D3QN is better
than DQN in that stable locomotion and fast training and the
vision based DRL algorithms will be successfully able to use at
the other complex environments and applications.
