Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation

TL;DR

This paper clarifies the concept of memory in reinforcement learning agents, proposes a standardized evaluation methodology, and demonstrates its importance through empirical experiments to enable objective comparison of memory capabilities.

Contribution

It provides precise definitions of memory types in RL, categorizes agent memory, and introduces a standardized evaluation framework for memory assessment.

Findings

Standardized methodology improves evaluation consistency

Empirical results show the importance of proper memory assessment

Violation of methodology leads to unreliable memory judgments

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 use 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 vs. short-term memory and declarative vs. 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.