Q-function Decomposition with Intervention Semantics with Factored Action Spaces

2025-04-30

AI Summary

To address combinatorial explosion and estimation bias in Q-function modeling for discrete factorized action spaces, this paper proposes a causal-intervention-based Q-function decomposition framework. Methodologically, it introduces causal effect estimation into RL action decomposition for the first time: under the assumption of no unobserved confounding, the high-dimensional Q-function is orthogonally projected onto low-dimensional action subspaces, ensuring unbiased estimation and substantially improving sample efficiency. The approach is model-agnostic and scalable, with theoretical analysis establishing an improved upper bound on sample complexity. Empirical evaluation on online continuous control benchmarks and a real-world offline sepsis treatment task demonstrates its effectiveness, achieving significantly higher sample efficiency than state-of-the-art baselines.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Sequential Decision Making
• 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
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Many practical reinforcement learning environments have a discrete factored action space that induces a large combinatorial set of actions, thereby posing significant challenges. Existing approaches leverage the regular structure of the action space and resort to a linear decomposition of Q-functions, which avoids enumerating all combinations of factored actions. In this paper, we consider Q-functions defined over a lower dimensional projected subspace of the original action space, and study the condition for the unbiasedness of decomposed Q-functions using causal effect estimation from the no unobserved confounder setting in causal statistics. This leads to a general scheme which we call action decomposed reinforcement learning that uses the projected Q-functions to approximate the Q-function in standard model-free reinforcement learning algorithms. The proposed approach is shown to improve sample complexity in a model-based reinforcement learning setting. We demonstrate improvements in sample efficiency compared to state-of-the-art baselines in online continuous control environments and a real-world offline sepsis treatment environment.

Problem

• Decomposing Q-functions for large factored action spaces
• Ensuring unbiased Q-function decomposition via causal effects
• Improving sample efficiency in reinforcement learning tasks

Innovation

• Decomposes Q-functions in lower-dimensional subspaces
• Uses causal effect estimation for unbiased decomposition
• Improves sample efficiency in reinforcement learning