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arXiv · 2411.07954

Learning Memory Mechanisms for Decision Making through Demonstrations

William Yue·Bo Liu·Peter Stone
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Abstract

In Partially Observable Markov Decision Processes, integrating an agent's history into memory poses a significant challenge for decision-making. Traditional imitation learning, relying on observation-action pairs for expert demonstrations, fails to capture the expert's memory mechanisms used in decision-making. To capture memory processes as demonstrations, we introduce the concept of memory dependency pairs $(p, q)$ indicating that events at time $p$ are recalled for decision-making at time $q$. We introduce AttentionTuner to leverage memory dependency pairs in Transformers and find significant improvements across several tasks compared to standard Transformers when evaluated on Memory Gym and the Long-term Memory Benchmark. Code is available at https://github.com/WilliamYue37/AttentionTuner.

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William Yue, Bo Liu, Peter Stone. 2024-11-12. Learning Memory Mechanisms for Decision Making through Demonstrations. https://arxiv.org/abs/2411.07954

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