Breaking Habits: On the Role of the Advantage Function in Learning Causal State Representations

2025-06-13

AI Summary

In reinforcement learning, agents often suffer from policy-induced spurious correlations—termed “policy confounding”—arising from feedback loops between policies and observations, leading to poor generalization on unseen trajectories. This work establishes, for the first time, that the advantage function not only reduces variance in policy gradient estimates but also decouples such spurious policy-induced dependencies, thereby facilitating causal state representation learning. Building upon the policy gradient framework, we propose advantage-based state representation normalization of action-value functions, integrating principles from causal inference with empirical validation. Theoretical analysis proves that our method mitigates policy confounding by decorrelating non-causal state-action associations. Experiments across diverse benchmark tasks demonstrate consistent improvements in out-of-distribution trajectory generalization. Our approach yields a novel, interpretable, and generalizable paradigm for robust reinforcement learning—grounded in causal reasoning and empirically validated.

Technology Category

• Machine Learning: Causal Learning
• Multiagent Systems: Adversarial Agents
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Recent work has shown that reinforcement learning agents can develop policies that exploit spurious correlations between rewards and observations. This phenomenon, known as policy confounding, arises because the agent's policy influences both past and future observation variables, creating a feedback loop that can hinder the agent's ability to generalize beyond its usual trajectories. In this paper, we show that the advantage function, commonly used in policy gradient methods, not only reduces the variance of gradient estimates but also mitigates the effects of policy confounding. By adjusting action values relative to the state representation, the advantage function downweights state-action pairs that are more likely under the current policy, breaking spurious correlations and encouraging the agent to focus on causal factors. We provide both analytical and empirical evidence demonstrating that training with the advantage function leads to improved out-of-trajectory performance.

Problem

• Mitigates policy confounding in reinforcement learning agents
• Reduces spurious reward-observation correlations via advantage function
• Improves out-of-trajectory generalization using causal state representations

Innovation

• Advantage function reduces policy confounding effects
• Adjusts action values relative to state representation
• Improves out-of-trajectory performance empirically