Title: Trajectory First: A Curriculum for Discovering Diverse Policies
Primary area: reinforcement learning
Keywords: Reinforcement Learning, Exploration, Diversity, Robotics

Abstract: Being able to solve a task in diverse ways makes agents more robust to task variations and less prone to local optima.
In this context, constrained diversity optimization has emerged as a powerful reinforcement learning (RL) framework to train a diverse set of agents in parallel.
However, existing constrained-diversity RL methods often under-explore in complex tasks such as robotic manipulation, leading to a lack in policy diversity.
To improve diversity optimization in RL, we therefore propose a two-stage curriculum.
The key idea of our method is to leverage a spline-based trajectory prior as an inductive bias to generate diverse, high-reward behaviors in the first stage, before learning step-based policies in the second.
In our empirical evaluation, we provide novel insights into  shortcomings of skill-based diversity optimization, and demonstrate empirically that our curriculum improves the diversity of the learned skills.