Shaping in humans and animals has been shown to be a powerful tool for
learning complex tasks as compared to learning in a randomized fashion. This
makes the problem less complex and enables one to solve the easier sub task at
hand first. Generating a curriculum for such guided learning involves
subjecting the agent to easier goals first, and then gradually increasing their
difficulty. This paper takes a similar direction and proposes a dual curriculum
scheme for solving robotic manipulation tasks with sparse rewards, called
MaMiC. It includes a macro curriculum scheme which divides the task into
multiple sub-tasks followed by a micro curriculum scheme which enables the
agent to learn between such discovered sub-tasks. We show how combining macro
and micro curriculum strategies help in overcoming major exploratory
constraints considered in robot manipulation tasks without having to engineer
any complex rewards. We also illustrate the meaning of the individual curricula
and how they can be used independently based on the task. The performance of
such a dual curriculum scheme is analyzed on the Fetch environments.Comment: To appear in the Proceedings of the 18th International Conference on
Autonomous Agents and Multiagent Systems (AAMAS 2019). (Extended Abstract
