In recent years, reinforcement learning (RL) has shown great potential for solving tasks in well-defined environments like games or robotics. This paper aims to solve the robotic reaching task in a simulation run on the Neurorobotics Platform (NRP). The target position is initialized randomly and the robot has 6 degrees of freedom. We compare the performance of various state-of-the-art model-free algorithms. At first, the agent is trained on ground truth data from the simulation to reach the target position in only one continuous movement. Later the complexity of the task is increased by using image data as input from the simulation environment. Experimental results show that training efficiency and results can be improved with appropriate dynamic training schedule function for curriculum learning.

翻译：近年来,强化学习(RL)显示出在游戏或机器人等明确界定的环境中解决任务的巨大潜力。本文的目的是在神经机能学平台(NRP)的模拟运行中解决机器人到达任务。目标位置是随机初始化的,机器人有6度的自由度。我们比较了各种最先进的无模型算法的性能。首先,该代理商通过模拟得到地面真相数据培训,在一个连续的运动中到达目标位置。后来,通过将图像数据作为模拟环境的投入,任务的复杂性增加了。实验结果显示,通过适当的动态培训课程学习功能,培训效率和成果是可以提高的。