Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning

📄 arXiv: 2505.03172v1 📥 PDF

作者: Caleb Chuck, Fan Feng, Carl Qi, Chang Shi, Siddhant Agarwal, Amy Zhang, Scott Niekum

分类: cs.LG, cs.AI

发布日期: 2025-05-06

备注: Published at ICLR 2025

期刊: The Thirteenth International Conference on Learning Representations. 2025

💡 一句话要点

提出HInt以解决目标导向强化学习中的稀疏奖励问题

🎯 匹配领域: 支柱一：机器人控制 (Robot Control) 支柱二：RL算法与架构 (RL & Architecture)

关键词: 目标导向强化学习 事后重标定 对象间交互 无效反事实 样本效率 动态机器人 强化学习

📋 核心要点

  1. 现有的事后重标定方法在对象中心领域中表现不佳，容易导致学习失败。
  2. 本文提出HInt方法，通过结合对象间的交互来改善样本效率，利用无效反事实的概念推断交互。
  3. 实验结果表明，HInt在Robosuite、Robot Air Hockey和Franka Kitchen等动态机器人领域中，样本效率提升可达4倍。

📝 摘要（中文）

回顾过去，事后重标定是克服目标导向强化学习（GCRL）中稀疏奖励的有效工具，尤其在导航和运动等领域。然而，在以对象为中心的领域中，事后重标定面临挑战。本文提出了基于交互的事后重标定方法HInt，结合对象间的交互来提升样本效率。通过定义“无效反事实”来推断交互，HInt在多个动态机器人领域中显著提高了样本效率，提升幅度可达4倍。

🔬 方法详解

问题定义：本文旨在解决目标导向强化学习中，事后重标定在对象中心领域的不足，特别是高奖励轨迹与实际目标之间的稀疏性问题。现有方法往往无法有效利用对象间的交互信息，导致学习效率低下。

核心思路：论文提出的HInt方法结合了对象间的交互与事后重标定，通过定义“无效反事实”来推断交互，从而提升样本效率。这种设计旨在确保学习过程中能够关注到有意义的对象间交互。

技术框架：整体架构包括两个主要模块：首先是无效反事实交互推断（NCII），通过学习模型进行交互推断；其次是结合交互信息的事后重标定，优化样本使用效率。

关键创新：最重要的创新在于提出了基于无效反事实的交互定义，使得对象间的交互可以被有效推断。这一方法与传统的事后重标定方法本质上不同，后者未能充分考虑对象间的动态关系。

关键设计：在技术细节上，模型的损失函数设计考虑了交互的推断准确性，网络结构采用了适应性学习机制，以便在不同的动态环境中进行有效的交互推断。

📊 实验亮点

实验结果显示，HInt在Robosuite、Robot Air Hockey和Franka Kitchen等多个动态机器人领域中，样本效率提升可达4倍，显著优于传统的事后重标定方法，验证了其在对象中心任务中的有效性。

🎯 应用场景

该研究的潜在应用领域包括机器人导航、物体操控和人机交互等场景。通过提升目标导向强化学习的样本效率，HInt能够加速机器人在复杂环境中的学习过程，具有重要的实际价值和广泛的应用前景。

📄 摘要（原文）

Hindsight relabeling is a powerful tool for overcoming sparsity in goal-conditioned reinforcement learning (GCRL), especially in certain domains such as navigation and locomotion. However, hindsight relabeling can struggle in object-centric domains. For example, suppose that the goal space consists of a robotic arm pushing a particular target block to a goal location. In this case, hindsight relabeling will give high rewards to any trajectory that does not interact with the block. However, these behaviors are only useful when the object is already at the goal -- an extremely rare case in practice. A dataset dominated by these kinds of trajectories can complicate learning and lead to failures. In object-centric domains, one key intuition is that meaningful trajectories are often characterized by object-object interactions such as pushing the block with the gripper. To leverage this intuition, we introduce Hindsight Relabeling using Interactions (HInt), which combines interactions with hindsight relabeling to improve the sample efficiency of downstream RL. However because interactions do not have a consensus statistical definition tractable for downstream GCRL, we propose a definition of interactions based on the concept of null counterfactual: a cause object is interacting with a target object if, in a world where the cause object did not exist, the target object would have different transition dynamics. We leverage this definition to infer interactions in Null Counterfactual Interaction Inference (NCII), which uses a "nulling'' operation with a learned model to infer interactions. NCII is able to achieve significantly improved interaction inference accuracy in both simple linear dynamics domains and dynamic robotic domains in Robosuite, Robot Air Hockey, and Franka Kitchen and HInt improves sample efficiency by up to 4x.