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Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents

Published 29 Sep 2023 in cs.LG | (2309.17207v6)

Abstract: Memory Gym presents a suite of 2D partially observable environments, namely Mortar Mayhem, Mystery Path, and Searing Spotlights, designed to benchmark memory capabilities in decision-making agents. These environments, originally with finite tasks, are expanded into innovative, endless formats, mirroring the escalating challenges of cumulative memory games such as "I packed my bag". This progression in task design shifts the focus from merely assessing sample efficiency to also probing the levels of memory effectiveness in dynamic, prolonged scenarios. To address the gap in available memory-based Deep Reinforcement Learning baselines, we introduce an implementation within the open-source CleanRL library that integrates Transformer-XL (TrXL) with Proximal Policy Optimization. This approach utilizes TrXL as a form of episodic memory, employing a sliding window technique. Our comparative study between the Gated Recurrent Unit (GRU) and TrXL reveals varied performances across our finite and endless tasks. TrXL, on the finite environments, demonstrates superior effectiveness over GRU, but only when utilizing an auxiliary loss to reconstruct observations. Notably, GRU makes a remarkable resurgence in all endless tasks, consistently outperforming TrXL by significant margins. Website and Source Code: https://marcometer.github.io/jmlr_2024.github.io/

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Summary

  • The paper introduces Memory Gym, a suite of endless environments designed to benchmark agent memory effectiveness over long periods and cumulative tasks, moving beyond sample efficiency.
  • Empirical results show that GRU-based memory mechanisms surprisingly outperformed Transformer-XL in endless memory tasks, challenging assumptions about attention vs. recurrence for long-term memory.
  • The findings suggest current DRL benchmarks may not fully capture capabilities needed for applications requiring robust, long-term memory and point to the need for new evaluation metrics and architectural research.

Memory Gym: Evaluating the Memory Effectiveness of Agents

The paper "Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents" by Pleines, Pallasch, Zimmer, and Preuss introduces a set of environments designed to evaluate the memory effectiveness of decision-making agents. This work introduces a suite of three primary environments—Mortar Mayhem, Mystery Path, and Searing Spotlights—crafted to assess the agents' capabilities to retain and utilize memory over extended periods of engagement. The authors address a critical need for benchmarks that emphasize not merely sample efficiency but also the agents' ability to effectively use memory in dynamic scenarios.

Key Contributions

The study's primary contribution lies in the development of endless tasks that simulate cumulative memory games. These tasks provide an incremental increase in difficulty as the agent progresses, thus serving as an automatic curriculum. By doing so, they assess not only sample efficiency, which is traditionally measured in reinforcement learning, but also memory effectiveness over prolonged engagements. The environments exhibit a dynamic, continuous nature aimed at challenging an agent's memory retention and recall capabilities substantially beyond that of finite tasks.

To conduct these experiments, the authors extend the capabilities of existing Deep Reinforcement Learning (DRL) algorithms. Specifically, they introduce an open-source implementation that combines Transformer-XL (TrXL) with Proximal Policy Optimization (PPO). This novel combination leverages TrXL as a form of episodic memory with a sliding window technique, aiming to enhance the agent's memory utility in the decision-making process.

Observations and Findings

Empirical results showcase varied agent performance, depending on the environment and task configuration. Within finite environments, the TrXL variant exhibits superior sample efficiency in certain tasks but reveals limitations in others. In particular, TrXL displayed notable sample efficiency and effectiveness benefits in the "Mystery Path" environment. However, in "Searing Spotlights," a GRU-based memory mechanism outperformed the Transformer-based architecture in terms of sample efficiency.

Notably, a pivotal and unexpected finding was the GRU's consistently superior performance in endless tasks. Despite TrXL's architectural advantages in sample efficiency in finite settings, GRU mechanisms surpassed Transformer-XL's effectiveness in memory retention for extended tasks. This observation challenges the conventional reliance on attention mechanisms over recurrence, emphasizing the necessity to reevaluate memory architectures under continuous task stress.

Implications and Future Directions

The findings within endless tasks suggest that current benchmarks might not fully encapsulate the broader capabilities needed for application-based scenarios where memory effectiveness takes precedence over mere interaction efficiency. This points to potential recalibrations needed in how DRL environments are structured for comprehensive agent evaluation.

The research emphasizes avenues for future work, particularly in addressing the bottlenecks identified in transformer architectures. It speculates on the broader adoption of emerging sequences models, such as structured state space sequence models, and other novel architectures that might exhibit enhanced performance over endless tasks.

In practicality, this work underlines the necessity for researchers to develop more comprehensive evaluation metrics that factor in memory effectiveness. The open-source nature of the baseline implementation presented here provides a blueprint for further advancements in this area. This resource enables the community to embark on more extensive investigations into agent memory capacities under prolonged trials, which might impact fields requiring autonomous decision systems with robust memory demands.

Overall, the introduction of Memory Gym establishes a new ground for evaluating memory effectiveness in AI, prompting existing architecture paradigms to evolve towards sustainable and memory-intensive applications.

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