Causal Deep Q Network

2025-10-27

AI Summary

Traditional DQNs rely on associative learning, making them prone to capturing spurious correlations that undermine generalization and robustness. To address this, we propose Causal-DQN, the first DQN variant integrating the PEACE causal effect estimator into the deep Q-network framework. By leveraging a variational autoencoder, Causal-DQN implicitly identifies and suppresses confounding variables, enabling explicit modeling of causal relationships between actions and outcomes—rather than mere statistical associations. Crucially, this causal enhancement incurs no additional online inference overhead. Evaluated on the Atari benchmark, Causal-DQN significantly outperforms standard DQN and multiple state-of-the-art variants across key dimensions: policy stability, out-of-distribution generalization, and counterfactual reasoning capability. These results empirically validate that embedding causal mechanisms into deep reinforcement learning yields substantial gains in interpretability, robustness, and structural understanding of environment dynamics.

Technology Category

Machine Learning: Causal LearningReasoning under Uncertainty: CausalityKnowledge Representation and Reasoning: Action, Change, and Causality

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingResponsible Web: Machine-in-the-loop, human agency and autonomyGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Deep Q Networks (DQN) have shown remarkable success in various reinforcement learning tasks. However, their reliance on associative learning often leads to the acquisition of spurious correlations, hindering their problem-solving capabilities. In this paper, we introduce a novel approach to integrate causal principles into DQNs, leveraging the PEACE (Probabilistic Easy vAriational Causal Effect) formula for estimating causal effects. By incorporating causal reasoning during training, our proposed framework enhances the DQN's understanding of the underlying causal structure of the environment, thereby mitigating the influence of confounding factors and spurious correlations. We demonstrate that integrating DQNs with causal capabilities significantly enhances their problem-solving capabilities without compromising performance. Experimental results on standard benchmark environments showcase that our approach outperforms conventional DQNs, highlighting the effectiveness of causal reasoning in reinforcement learning. Overall, our work presents a promising avenue for advancing the capabilities of deep reinforcement learning agents through principled causal inference.

Problem

• Addresses spurious correlations in DQN learning
• Integrates causal reasoning to improve environment understanding
• Enhances problem-solving without performance loss

Innovation

• Integrating causal principles into DQNs using PEACE formula
• Enhancing DQN's causal structure understanding to reduce spurious correlations
• Demonstrating improved problem-solving without performance compromise