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arXiv · 2506.01568

Trajectory First: A Curriculum for Discovering Diverse Policies

Cornelius V. Braun·Sayantan Auddy·Marc Toussaint
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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 become a useful reinforcement learning (RL) framework for training a set of diverse agents in parallel. However, existing constrained-diversity RL methods often under-explore in complex tasks such as robot manipulation, resulting in limited behavioral diversity. We address this with a two-stage curriculum that introduces a spline-based trajectory prior as an inductive bias to produce diverse, high-reward behaviors in an initial stage, and then distills these behaviors into reactive, step-wise policies in a second stage. In our empirical evaluation, we provide novel insights into challenges of diversity-targeted training and show that our curriculum increases the diversity of learned skills while maintaining high task performance.

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Cornelius V. Braun, Sayantan Auddy, Marc Toussaint. 2026-08-13. Trajectory First: A Curriculum for Discovering Diverse Policies. https://arxiv.org/abs/2506.01568

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