Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation
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Abstract
The incorporation of memory into agents is essential for numerous tasks within the domain of Reinforcement Learning (RL). In particular, memory is paramount for tasks that require the utilization of past information, adaptation to novel environments, and improved sample efficiency. However, the term ``memory'' encompasses a wide range of concepts, which, coupled with the lack of a unified methodology for validating an agent's memory, leads to erroneous judgments about agents' memory capabilities and prevents objective comparison with other memory-enhanced agents. This paper aims to streamline the concept of memory in RL by providing practical precise definitions of agent memory types, such as long-term versus short-term memory and declarative versus procedural memory, inspired by cognitive science. Using these definitions, we categorize different classes of agent memory, propose a robust experimental methodology for evaluating the memory capabilities of RL agents, and standardize evaluations. Furthermore, we empirically demonstrate the importance of adhering to the proposed methodology when evaluating different types of agent memory by conducting experiments with different RL agents and what its violation leads to.
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In this study, we formalize memory types in RL, distinguishing long-term memory (LTM) from short-term memory (STM), and declarative from procedural memory, drawing inspiration from neuroscience. We also separate POMDPs into two classes: Memory Decision-Making (Memory DM) and Meta Reinforcement Learning (Meta-RL).
The formalization, along with the methodology for validating LTM and STM in the Memory DM framework, provides a clear structure for distinguishing between different types of agent memory. This enables fair comparisons of agents with similar memory mechanisms and highlights limitations in memory architecture, facilitating precise evaluations and improvements.
Additionally, we demonstrate the potential pitfalls of neglecting this methodology. Misconfigured experiments can lead to misleading conclusions about an agent’s memory capabilities, blurring the lines between LTM and STM. By following our approach, researchers can achieve more reliable assessments and make informed comparisons between memory-enhanced agents.
This work provides a significant step toward a unified understanding of agent memory in RL. Our definitions and methodology offer practical tools for rigorously testing agent memory, ensuring consistent experimental design. By addressing common inconsistencies, our approach guarantees reliable results and meaningful comparisons, advancing research in RL.
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