  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.
