Synthetic POMDPs to Challenge Memory-Augmented RL: Memory Demand Structure Modeling

• URL: http://arxiv.org/abs/2508.04282v1
• Date: Wed, 06 Aug 2025 10:13:17 GMT
• Title: Synthetic POMDPs to Challenge Memory-Augmented RL: Memory Demand Structure Modeling
• Authors: Yongyi Wang, Lingfeng Li, Bozhou Chen, Ang Li, Hanyu Liu, Qirui Zheng, Xionghui Yang, Wenxin Li,
• Abstract summary: Recent research has developed benchmarks for memory-augmented reinforcement learning (RL) algorithms.<n>POMDP environments where agents depend on past observations to make decisions.<n>Our work clarifies the challenges of memory-augmented RL in solving POMDPs, provides guidelines for analyzing and designing POMDP environments, and offers empirical support for selecting memory models in RL tasks.
• Score: 6.279650855031215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent research has developed benchmarks for memory-augmented reinforcement learning (RL) algorithms, providing Partially Observable Markov Decision Process (POMDP) environments where agents depend on past observations to make decisions. While many benchmarks incorporate sufficiently complex real-world problems, they lack controllability over the degree of challenges posed to memory models. In contrast, synthetic environments enable fine-grained manipulation of dynamics, making them critical for detailed and rigorous evaluation of memory-augmented RL. Our study focuses on POMDP synthesis with three key contributions: 1. A theoretical framework for analyzing POMDPs, grounded in Memory Demand Structure (MDS), transition invariance, and related concepts; 2. A methodology leveraging linear process dynamics, state aggregation, and reward redistribution to construct customized POMDPs with predefined properties; 3. Empirically validated series of POMDP environments with increasing difficulty levels, designed based on our theoretical insights. Our work clarifies the challenges of memory-augmented RL in solving POMDPs, provides guidelines for analyzing and designing POMDP environments, and offers empirical support for selecting memory models in RL tasks.

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