Deep Reinforcement Learning agents often suffer from catastrophic forgetting,
forgetting previously found solutions in parts of the input space when training
on new data. Replay Memories are a common solution to the problem,
decorrelating and shuffling old and new training samples. They naively store
state transitions as they come in, 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 paper shows that map-based experience replay allows for significant memory
reduction with only small performance decreases.

该研究采用基于心理认知的重新记忆策略，通过构建一个基于地图的经验重播存储库，减少了记忆体的大小，并增加了样本之间的相关性，从而有效地解决了深度增强学习代理在处理新数据时可能出现的忘记先前解决方案的问题。