Goal Discovery with Causal Capacity for Efficient Reinforcement Learning

📄 arXiv: 2508.09624v1 📥 PDF

作者: Yan Yu, Yaodong Yang, Zhengbo Lu, Chengdong Ma, Wengang Zhou, Houqiang Li

分类: cs.LG, cs.AI

发布日期: 2025-08-13

💡 一句话要点

提出因果能力目标发现框架以提升强化学习效率

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 因果推断 强化学习 目标发现 智能体探索 多目标任务 蒙特卡洛方法

📋 核心要点

  1. 现有方法在复杂场景中难以有效测量因果关系，导致智能体探索效率低下。
  2. 提出因果能力度量，识别关键决策点，并将其作为子目标引导智能体进行有目的的探索。
  3. 实验证明，GDCC在多目标任务中显著提高成功率，相较于基线方法表现更佳。

📝 摘要（中文）

因果推断对人类探索世界至关重要，可以通过建模使智能体在强化学习中高效探索环境。现有研究表明，建立动作与状态转移之间的因果关系能够增强智能体推理政策如何影响未来轨迹，从而促进有针对性的探索。然而，由于复杂场景中状态-动作空间的庞大，因果关系的测量面临挑战。本文提出了一种新颖的因果能力目标发现框架（GDCC），通过推导状态空间中的因果能力度量，识别关键决策点，优化探索过程。实验证明，具有高因果能力的状态与预期子目标一致，GDCC在多目标任务中显著提高了成功率。

🔬 方法详解

问题定义：本文旨在解决在复杂环境中因果关系测量的困难，现有方法无法有效引导智能体进行高效探索。

核心思路：提出因果能力度量，表示智能体行为对未来轨迹的最大影响，通过识别关键决策点来优化探索过程。

技术框架：GDCC框架包括因果能力的计算、关键点识别和探索优化三个主要模块，采用蒙特卡洛方法进行关键点的识别。

关键创新：最重要的创新在于引入因果能力度量，能够在高维连续环境中有效识别智能体的关键决策点，与现有方法相比，提供了更具针对性的探索策略。

关键设计：在技术细节上，采用蒙特卡洛方法进行关键点识别，并针对离散和连续状态空间进行了优化，确保在复杂环境中保持高效性。

📊 实验亮点

实验结果显示，GDCC在多目标任务中成功率显著提高，尤其是在高因果能力状态下，相较于基线方法提升幅度达到XX%（具体数据待补充）。

🎯 应用场景

该研究的潜在应用领域包括机器人导航、自动驾驶、游戏智能体等，能够有效提升智能体在复杂环境中的探索效率，具有重要的实际价值和未来影响。

📄 摘要（原文）

Causal inference is crucial for humans to explore the world, which can be modeled to enable an agent to efficiently explore the environment in reinforcement learning. Existing research indicates that establishing the causality between action and state transition will enhance an agent to reason how a policy affects its future trajectory, thereby promoting directed exploration. However, it is challenging to measure the causality due to its intractability in the vast state-action space of complex scenarios. In this paper, we propose a novel Goal Discovery with Causal Capacity (GDCC) framework for efficient environment exploration. Specifically, we first derive a measurement of causality in state space, \emph{i.e.,} causal capacity, which represents the highest influence of an agent's behavior on future trajectories. After that, we present a Monte Carlo based method to identify critical points in discrete state space and further optimize this method for continuous high-dimensional environments. Those critical points are used to uncover where the agent makes important decisions in the environment, which are then regarded as our subgoals to guide the agent to make exploration more purposefully and efficiently. Empirical results from multi-objective tasks demonstrate that states with high causal capacity align with our expected subgoals, and our GDCC achieves significant success rate improvements compared to baselines.