Trajectory First: A Curriculum for Discovering Diverse Policies

2025/06/02 by Braun, Cornelius V., Auddy, Sayantan, Toussaint, Marc
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2506.01568

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.

Related

• Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
• Iteratively Learn Diverse Strategies with State Distance Information
• Learning Diverse Skills for Local Navigation under Multi-constraint Optimality
• Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
• Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality