Deep reinforcement learning (RL) agents often suffer from catastrophic forgetting, forgetting previously found solutions in parts of the input space when training new data. Replay memories are a common solution to the problem by decorrelating and shuffling old and new training samples. They naively store state transitions as they arrive, without regard for redundancy. We introduce a novel cognitive-inspired replay memory approach based on the Grow-When-Required (GWR) self-organizing network, which resembles a map-based mental model of the world. Our approach organizes stored transitions into a concise environment-model-like network of state nodes and transition edges, merging similar samples to reduce the memory size and increase pair-wise distance among samples, which increases the relevancy of each sample. Overall, our study shows that map-based experience replay allows for significant memory reduction with only small decreases in performance.
CITATION STYLE
Hafez, M. B., Immisch, T., Weber, T., & Wermter, S. (2023). Map-based experience replay: a memory-efficient solution to catastrophic forgetting in reinforcement learning. Frontiers in Neurorobotics, 17. https://doi.org/10.3389/fnbot.2023.1127642
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