Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces

TL;DR

This paper introduces Dynamic Neighborhood Construction (DNC), a scalable method for efficiently exploring structured large discrete action spaces in reinforcement learning, capable of handling sizes up to 10^73 actions and outperforming existing approaches.

Contribution

The paper proposes a novel DNC paradigm and heuristic for structured large discrete action spaces, enabling scalable exploration beyond current benchmarks.

Findings

DNC matches or outperforms state-of-the-art methods.

DNC efficiently explores extremely large action spaces.

Method scales to intractably large action spaces.

Abstract

Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning. Existing solution approaches can handle unstructured LDAS with up to a few million actions. However, many real-world applications in logistics, production, and transportation systems have combinatorial action spaces, whose size grows well beyond millions of actions, even on small instances. Fortunately, such action spaces exhibit structure, e.g., equally spaced discrete resource units. With this work, we focus on handling structured LDAS (SLDAS) with sizes that cannot be handled by current benchmarks: we propose Dynamic Neighborhood Construction (DNC), a novel exploitation paradigm for SLDAS. We present a scalable neighborhood exploration heuristic that utilizes this paradigm and efficiently explores the discrete neighborhood around the continuous proxy action in structured action spaces with up to $10^{73}$ actions. We demonstrate the performance of our method by benchmarking it against three state-of-the-art approaches designed for large discrete action spaces across two distinct environments. Our results show that DNC matches or outperforms state-of-the-art approaches while being computationally more efficient. Furthermore, our method scales to action spaces that so far remained computationally intractable for existing methodologies.