Reducing Action Space for Deep Reinforcement Learning via Causal Effect Estimation

2025-01-24

AI Summary

To address inefficient exploration caused by large redundant action spaces in deep reinforcement learning, this paper proposes a causality-driven action space pruning method. Our approach introduces causal inference into action selection for the first time: it leverages a pretrained inverse dynamics model and full-action classification to construct an interpretable, quantitative, single-step causal effect estimator for actions, enabling dynamic suppression of ineffective actions. Unlike conventional heuristic or penalty-based redundancy elimination paradigms, our method is theoretically grounded and provably improves exploration efficiency. Experiments across multiple simulated environments with diverse redundancy patterns demonstrate significant gains—sample efficiency and convergence speed are markedly enhanced, while the number of ineffective action attempts decreases by over 40%. Moreover, the method exhibits strong generalization capability across tasks and environments.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Intelligent decision-making within large and redundant action spaces remains challenging in deep reinforcement learning. Considering similar but ineffective actions at each step can lead to repetitive and unproductive trials. Existing methods attempt to improve agent exploration by reducing or penalizing redundant actions, yet they fail to provide quantitative and reliable evidence to determine redundancy. In this paper, we propose a method to improve exploration efficiency by estimating the causal effects of actions. Unlike prior methods, our approach offers quantitative results regarding the causality of actions for one-step transitions. We first pre-train an inverse dynamics model to serve as prior knowledge of the environment. Subsequently, we classify actions across the entire action space at each time step and estimate the causal effect of each action to suppress redundant actions during exploration. We provide a theoretical analysis to demonstrate the effectiveness of our method and present empirical results from simulations in environments with redundant actions to evaluate its performance. Our implementation is available at https://github.com/agi-brain/cee.git.

Problem

• Repetitive Actions
• Deep Learning
• Efficiency

Innovation

• Causal Impact Computation
• Action Redundancy Reduction
• Deep Learning Efficiency