  The domain of robotics is challenging to apply deep reinforcement learning
due to the need for large amounts of data and for ensuring safety during
learning. Curriculum learning has shown good performance in terms of sample-
efficient deep learning. In this paper, we propose an algorithm (named GloCAL)
that creates a curriculum for an agent to learn multiple discrete tasks, based
on clustering tasks according to their evaluation scores. From the
highest-performing cluster, a global task representative of the cluster is
identified for learning a global policy that transfers to subsequently formed
new clusters, while the remaining tasks in the cluster are learned as local
policies. The efficacy and efficiency of our GloCAL algorithm are compared with
other approaches in the domain of grasp learning for 49 objects with varied
object complexity and grasp difficulty from the EGAD! dataset. The results show
that GloCAL is able to learn to grasp 100% of the objects, whereas other
approaches achieve at most 86% despite being given 1.5 times longer training
time.
