2 research outputs found
Achieving Goals using Reward Shaping and Curriculum Learning
Real-time control for robotics is a popular research area in the
reinforcement learning community. Through the use of techniques such as reward
shaping, researchers have managed to train online agents across a multitude of
domains. Despite these advances, solving goal-oriented tasks still requires
complex architectural changes or hard constraints to be placed on the problem.
In this article, we solve the problem of stacking multiple cubes by combining
curriculum learning, reward shaping, and a high number of efficiently
parallelized environments. We introduce two curriculum learning settings that
allow us to separate the complex task into sequential sub-goals, hence enabling
the learning of a problem that may otherwise be too difficult. We focus on
discussing the challenges encountered while implementing them in a
goal-conditioned environment. Finally, we extend the best configuration
identified on a higher complexity environment with differently shaped objects.Comment: To be published at Future Technologies Conference (FTC) 202