Synthetic POMDPs to Challenge Memory-Augmented RL: Memory Demand Structure Modeling
This work addresses the need for more rigorous evaluation tools for researchers in reinforcement learning, though it is incremental as it builds on existing synthetic environment approaches.
The paper tackled the lack of controllability in existing benchmarks for memory-augmented reinforcement learning by developing a theoretical framework and methodology to synthesize Partially Observable Markov Decision Processes with predefined difficulty levels, resulting in empirically validated environments that clarify challenges and provide guidelines for analysis and design.
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.