Trajectory First: A Curriculum for Discovering Diverse Policies

• URL: http://arxiv.org/abs/2506.01568v2
• Date: Wed, 30 Jul 2025 08:07:33 GMT
• Title: Trajectory First: A Curriculum for Discovering Diverse Policies
• Authors: Cornelius V. Braun, Sayantan Auddy, Marc Toussaint,
• Abstract summary: Being able to solve a task in diverse ways makes agents more robust to task variations and less prone to local optima.<n> constrained diversity optimization has emerged as a powerful reinforcement learning framework to train a diverse set of agents in parallel.<n>We propose a curriculum that first explores at the trajectory level before learning step-based policies.
• Score: 17.315583101484147
• License: http://creativecommons.org/licenses/by/4.0/
• 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 curriculum that first explores at the trajectory level before learning step-based policies. In our empirical evaluation, we provide novel insights into the shortcoming of skill-based diversity optimization, and demonstrate empirically that our curriculum improves the diversity of the learned skills.

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