通过轨迹先验训练多样化机器人策略，提升复杂任务适应性。
Trajectory First: A Curriculum for Discovering Diverse Policies
- 用样条轨迹先验引导初始阶段生成多样高奖励行为
- 在复杂操作任务中多样性提升47%，性能保持高位
- 适合需要多策略鲁棒性的机器人控制场景
能够以多种方式完成任务的智能体对任务变化更具鲁棒性，且不易陷入局部最优。在此背景下，约束多样性优化已成为并行训练多样化智能体的强化学习框架。然而，现有方法在复杂任务（如机器人操作）中常存在探索不足，导致行为多样性有限。本文提出两阶段课程：第一阶段引入基于样条的轨迹先验作为归纳偏置，生成多样且高奖励的行为；第二阶段将这些行为提炼为反应式、分步执行的策略。实验表明，该课程在多样性目标训练中揭示了新挑战，并显著提升了所学技能的多样性，同时维持了高水平的任务表现。
原文摘要 · Abstract (English)
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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