  Reinforcement learning has shown great promise in the training of robot
behavior due to the sequential decision making characteristics. However, the
required enormous amount of interactive and informative training data provides
the major stumbling block for progress. In this study, we focus on accelerating
reinforcement learning (RL) training and improving the performance of
multi-goal reaching tasks. Specifically, we propose a precision-based
continuous curriculum learning (PCCL) method in which the requirements are
gradually adjusted during the training process, instead of fixing the parameter
in a static schedule. To this end, we explore various continuous curriculum
strategies for controlling a training process. This approach is tested using a
Universal Robot 5e in both simulation and real-world multi-goal reach
experiments. Experimental results support the hypothesis that a static training
schedule is suboptimal, and using an appropriate decay function for curriculum
learning provides superior results in a faster way.
