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文章背景与核心概要

强化学习（RL）中的信用分配问题（Credit Assignment Problem, CAP）是长期以来制约智能体高效学习的核心挑战，其核心在于如何准确评估智能体在不同时间点的动作对最终结果的贡献或责任。传统的基于时间邻近性或事后奖励重加权的方法，往往无法有效区分智能体的真实策略（能力）与环境的随机波动（运气）。为了解决这一根本痛点，本文引入了一种基于因果理论的框架——反事实沙apley信用分配（Counterfactual Shapley Credit Assignment），通过利用反事实沙apley值（\(\phi\)-value）来重新分配奖励。

该研究的核心技术在于利用 \(\phi\)-value 消除虚假关联，精准捕捉稀疏因果关系、应对高度随机性并解决延迟奖励问题，同时确保最优策略保持不变。为此，作者推导出一个能够高效计算 \(\phi\)-value 的一致性估计器，并在此基础上开发了新型策略梯度方法 \(\phi\)-PPO，结合优先轨迹回放（PTR）。实验结果表明，该方法在复杂环境中展现出卓越的样本效率，能够精确对齐任务奖励的真实因果驱动因素，显著优于现有前沿方法。

Counterfactual Shapley Credit Assignment

Summary

The Credit Assignment Problem (CAP) is a fundamental challenge in Reinforcement Learning (RL), focusing on how to accurately distribute rewards or blame for outcomes across an agent's actions over time. Traditional methods—such as those relying on temporal contiguity or hindsight-conditioned reward reweighting—frequently struggle to differentiate between an agent's actual skill (policy) and random environmental stochasticity (luck).

To solve this, Counterfactual Shapley Credit Assignment introduces a causal-theoretic framework that leverages the Counterfactual Shapley Value (\(\phi\)-value). By redistributing rewards based on true causal drivers rather than spurious correlations, \(\phi\)-values improve temporal credit assignment across three major hurdles: 1. Sparse causality 2. High stochasticity 3. Delayed rewards

The paper introduces a consistent estimator for efficiently computing \(\phi\)-values, a new policy gradient method called \(\phi\)-PPO, and Prioritized Trajectory Replay (PTR). Empirical results show that this approach achieves superior sample efficiency and aligns precisely with ground-truth causes of task rewards, outperforming state-of-the-art methods in complex environments.

信用分配问题（Credit Assignment Problem, CAP）是强化学习（RL）中的一个基础性挑战，其核心在于如何准确地将结果的奖励或责任分配给智能体在不同时间点所采取的动作。传统方法（例如依赖时间邻近性或事后条件奖励重加权的方法）往往难以区分智能体的真实技能（策略）与环境的随机性（运气）。

为了解决这一问题，“反事实沙apley信用分配”（Counterfactual Shapley Credit Assignment）引入了一个基于因果理论的框架，利用反事实沙apley值（\(\phi\)-value）来进行信用分配。通过根据真实的因果驱动因素而非虚假关联来重新分配奖励，\(\phi\)-value 改善了三个主要障碍中的时间信用分配： 1. 稀疏因果关系 2. 高随机性 3. 延迟奖励

本文引入了一种用于高效计算 \(\phi\)-value 的一致性估计器、一种名为 \(\phi\)-PPO 的新型策略梯度方法，以及优先轨迹回放（PTR）。实证结果表明，该方法在复杂的环境中实现了卓越的样本效率，并与任务奖励的真实成因精确对齐，表现优于当前最先进的方法。

Paper Metadata

Field Details
arXiv ID arXiv:2607.16999 [cs.LG]
Subjects Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Authors Mingxuan Li, Kai-Zhan Lee, Elias Bareinboim
Submitted 18 Jul 2026 (v1); Last revised 7 Aug 2026 (this version, v2)
Comments Reinforcement Learning Journal 2026 (Also RLC 2026)
DOI 10.48550/arXiv.2607.16999

论文元数据

字段 详情
arXiv ID arXiv:2607.16999 [cs.LG]
研究主题 机器学习 (cs.LG); 人工智能 (cs.AI)
作者 Mingxuan Li, Kai-Zhan Lee, Elias Bareinboim
提交时间 2026年7月18日 (v1)；最后修订于 2026年8月7日 (当前版本 v2)
评论 强化学习期刊 2026 (同时见 RLC 2026)
DOI 10.48550/arXiv.2607.16999

Abstract

The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents. Existing frameworks, whether relying on temporal contiguity or hindsight-conditioned reward reweighting, frequently fail to attribute properly between an agent's policy (skill) and environmental stochasticity (luck). A principled approach to CAP must isolate the true causal drivers of observed outcomes from spurious correlations and environmental randomness. We introduce Counterfactual Shapley Credit Assignment, a novel framework grounded in causal theory that attributes credit and blame via the Counterfactual Shapley Value (\(\phi\)-value). By redistributing environmental rewards, \(\phi\)-values enhance temporal credit assignment across three critical dimensions: sparse causality, high stochasticity, and delayed rewards, all while preserving the optimal policy. We derive a consistent estimator that computes \(\phi\)-values efficiently, enabling a new class of policy gradient methods, \(\phi\)-PPO, combined with Prioritized Trajectory Replay (PTR). Empirical results demonstrate that \(\phi\)-values align precisely to the ground truth causes of task rewards with superior sample efficiency in challenging environments where prior state-of-the-art methods fail to converge.

摘要

信用分配问题（CAP）对于开发高效且可解释的强化学习（RL）智能体至关重要。现有的框架，无论是依赖于时间邻近性还是事后条件奖励重加权，往往无法在智能体的策略（技能）与环境随机性（运气）之间做出恰当的归因。针对 CAP 的原则性方法必须将观察到结果的真实因果驱动因素与虚假关联及环境随机性隔离开来。我们引入了反事实沙apley信用分配，这是一个植根于因果理论的新颖框架，通过反事实沙apley值（\(\phi\)-value）来分配信用和责任。通过重新分配环境奖励，\(\phi\)-value 在三个关键维度上增强了时间信用分配：稀疏因果关系、高随机性和延迟奖励，同时保持最优策略不变。我们推导出了一个能够高效计算 \(\phi\)-value 的一致性估计器，从而催生了一类新的策略梯度方法 \(\phi\)-PPO，并结合了优先轨迹回放（PTR）。实证结果表明，在先前最先进的方法无法收敛的具有挑战性的环境中，\(\phi\)-value 能够精确对齐任务奖励的真实成因，并具有卓越的样本效率。

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