Map-based experience replay: a memory-efficient solution to catastrophic forgetting in reinforcement learning.

ABSTRACT: 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 releva

SUBMITTER: Hafez MB 

PROVIDER: S-EPMC10333526 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature