  Sample efficiency has been a key issue in reinforcement learning (RL). An
efficient agent must be able to leverage its prior experiences to quickly adapt
to similar, but new tasks and situations. Meta-RL is one attempt at formalizing
and addressing this issue. Inspired by recent progress in meta-RL, we introduce
BIMRL, a novel multi-layer architecture along with a novel brain-inspired
memory module that will help agents quickly adapt to new tasks within a few
episodes. We also utilize this memory module to design a novel intrinsic reward
that will guide the agent's exploration. Our architecture is inspired by
findings in cognitive neuroscience and is compatible with the knowledge on
connectivity and functionality of different regions in the brain. We
empirically validate the effectiveness of our proposed method by competing with
or surpassing the performance of some strong baselines on multiple MiniGrid
environments.
