Experiments on Motion Learning of Humanoid Robot with Reinforcement Learning by Policy Optimization

Abstract

Experiments on reinforcement learning were conducted on games on OpenAI Gym and robot simulators using "Proximal Policy Optimization Algorithms", which is considered to be suitable for motion learning of humanoid robots. As a result, it was confirmed that reinforcement learning is possible by the program of the algorithm published from OpenAI. Moreover, we confirmed that the operation on the robot simulator can be operated with real robot by the experimental experiment with real robot.