Synthetic POMDPs to Challenge Memory-Augmented RL: Memory Demand Structure Modeling
Summary: arXiv:2508.04282v3 Announce Type: replace
Abstract
Recent benchmarks for memory-augmented reinforcement learning (RL) have introduced partially observable Markov decision process (POMDP) environments in which agents must use historical observations to make decisions. However, these benchmarks often lack fine-grained control over the challenges posed to memory models. Synthetic environments offer a solution, enabling precise manipulation of environment dynamics for rigorous and interpretable evaluation of memory-augmented RL. This paper advances the design of such customizable POMDPs with three key contributions:
- A theoretical framework for analyzing POMDPs based on Memory Demand Structure (MDS) and related concepts.
- A methodology using linear dynamics, state aggregation, and reward redistribution to construct POMDPs with predefined MDS.
- A suite of lightweight, scalable POMDP environments with tunable difficulty, grounded in our theoretical insights.
Introduction
The rise of memory-augmented reinforcement learning has prompted researchers to explore new environments that better evaluate the memory capabilities of learning agents. Traditional benchmarks often fail to offer a nuanced perspective on the memory demands placed upon these agents. This paper introduces a framework for creating synthetic POMDP environments that allow for targeted analysis of memory requirements in reinforcement learning tasks.
Key Contributions
This study presents three significant contributions to the field of memory-augmented RL:
- Theoretical Framework: We propose a theoretical framework centered on Memory Demand Structure (MDS), providing a systematic way to analyze the memory requirements of agents in POMDP settings.
- Methodology for POMDP Construction: Our methodology employs linear dynamics, state aggregation, and reward redistribution techniques to design POMDPs that exhibit predefined MDS characteristics, thereby making it easier to assess the performance of different memory architectures.
- Scalable Environment Suite: We have developed a suite of lightweight and scalable POMDP environments, allowing researchers to adjust the difficulty level and tailor the challenges to their specific needs. This flexibility is crucial for interpreting results and understanding the core challenges in partially observable RL.
Implications for Future Research
By clarifying the core challenges associated with partially observable reinforcement learning, this work not only enhances the understanding of memory-augmented RL but also provides principled guidelines for POMDP design. The insights gained from our theoretical framework and methodologies can assist researchers in selecting and developing appropriate memory architectures suited for various RL tasks.
Conclusion
The introduction of synthetic POMDPs with a focus on Memory Demand Structure represents a significant leap forward in the evaluation of memory-augmented reinforcement learning models. As the field continues to evolve, the ability to manipulate and control the dynamics of POMDP environments will be invaluable for developing more effective and interpretable memory architectures.
Overall, this study serves as a foundational step toward better understanding the interplay between memory demands and reinforcement learning, paving the way for future advancements in the area.